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Journal articles on the topic 'Multi-agent paradigms'
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Patel, Rakesh B., and Satish K. Shah. "Multi-agent controller using soft computational paradigms." Indian Journal of Applied Research 3, no. 2 (October 1, 2011): 138–41. http://dx.doi.org/10.15373/2249555x/feb2013/48.
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HORLING, BRYAN, and VICTOR LESSER. "A survey of multi-agent organizational paradigms." Knowledge Engineering Review 19, no. 4 (December 2004): 281–316. http://dx.doi.org/10.1017/s0269888905000317.
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Many researchers have demonstrated that the organizational design employed by an agent system can have a significant, quantitative effect on its performance characteristics. A range of organizational strategies have emerged from this line of research, each with different strengths and weaknesses. In this article we present a survey of the major organizational paradigms used in multi-agent systems. These include hierarchies, holarchies, coalitions, teams, congregations, societies, federations, markets, and matrix organizations. We will provide a description of each, discuss their advantages and disadvantages, and provide examples of how they may be instantiated and maintained. This summary will facilitate the comparative evaluation of organizational styles, allowing designers to first recognize the spectrum of possibilities, and then guiding the selection of an appropriate organizational design for a particular domain and environment.
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Baykasoglu, Adil, Vahit Kaplanoglu, Rizvan Erol, and Cenk Sahin. "A MULTI-AGENT FRAMEWORK FOR LOAD CONSOLIDATION IN LOGISTICS." TRANSPORT 26, no. 3 (October 5, 2011): 320–28. http://dx.doi.org/10.3846/16484142.2011.622141.
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Logistics companies mainly provide land transportation services facing with difficulties in making effective operational decisions. This is especially the case of making load/capacity/route planning and load consolidation where customer orders are generally unpredictable and subject to sudden changes. Classical modelling and decision support systems are mostly insufficient for providing satisfactory solutions in a reasonable time solving such dynamic problems. Agent-based approaches, especially multi-agent paradigms that can be considered as relatively new members of system science and software engineering, are providing effective mechanisms for modelling dynamic systems generally operating under unpredictable environments and having a high degree of complex interactions. It seems that multi-agent paradigms have big potential for handling complex problems in land transportation logistics. Based on this motivation, the paper proposes a multi-agent based framework for load consolidation problems of third-party logistics companies.
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Surynek, Pavel. "Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12409–17. http://dx.doi.org/10.1609/aaai.v35i14.17472.
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We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MG-MAPF assigns each agent multiple goal vertices and the task is to visit each of them at least once. Solving MG-MAPF not only requires finding collision free paths for individual agents but also determining the order of visiting agent's goal vertices so that common objectives like the sum-of-costs are optimized. We suggest two novel algorithms using different paradigms to address MG-MAPF: a heuristic search-based algorithm called Hamiltonian-CBS (HCBS) and a compilation-based algorithm built using the satisfiability modulo theories (SMT), called SMT-Hamiltonian-CBS (SMT-HCBS).
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DARBYSHIRE, PAUL. "EFFECTS OF COMMUNICATION ON GROUP LEARNING RATES IN A MULTI-AGENT ENVIRONMENT." Advances in Complex Systems 06, no. 03 (September 2003): 405–26. http://dx.doi.org/10.1142/s0219525903000979.
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Distillations utilize multi-agent based modeling and simulation techniques to study warfare as a complex adaptive system at the conceptual level. The focus is placed on the interactions between the agents to facilitate study of cause and effect between individual interactions and overall system behavior. Current distillations do not utilize machine-learning techniques to model the cognitive abilities of individual combatants but employ agent control paradigms to represent agents as highly instinctual entities. For a team of agents implementing a reinforcement-learning paradigm, the rate of learning is not sufficient for agents to adapt to this hostile environment. However, by allowing the agents to communicate their respective rewards for actions performed as the simulation progresses, the rate of learning can be increased sufficiently to significantly increase the teams chances of survival. This paper presents the results of trials to measure the success of a team-based approach to the reinforcement-learning problem in a distillation, using reward communication to increase learning rates.
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Tokadlı, Güliz, Kaitlyn Ouverson, Chase Meusel, Austin Garcia, Stephen B. Gilbert, and Michael C. Dorneich. "An Analysis of Video Games Using the Dimensions of Human-Agent Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 716–20. http://dx.doi.org/10.1177/1541931218621163.
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Designers of human-agent interaction techniques may benefit from an analysis of existing video games that include aspects of human-agent teaming. Many popular multi-player video games have been designed to integrate multiple human and computer agents in pursuit of a common objective and can serve as a testbed to explore novel interaction methods in human-agent teams. A guiding framework of human-agent interaction was created to bridge best practices between video game and real-world domains. The framework was used to analyze games on five main dimensions: 1) Levels of Automation, 2) Levels of Interaction, 3) Control Mode, 4) Teaming, and 5) Interaction Timing. Two video games, Final Fantasy XIV and Mass Effect, were assessed to identify human-agent interaction paradigms, and ramifications for real-world applications for human-agent teaming. This research draws on interaction design principles, human-agent interaction theory, and existing video games to offer human-agent team designers potential examples of successful interaction paradigms.
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DE GRANDE, PABLO, and MANUEL EGUIA. "STRATEGY SPACE EXPLORATION OF A MULTI-AGENT MODEL FOR THE LABOR MARKET." International Journal of Modern Physics C 17, no. 03 (March 2006): 357–71. http://dx.doi.org/10.1142/s0129183106008534.
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We present a multi-agent system where typical labor market mechanisms emerge. Based on a few simple rules, our model allows for different interpretative paradigms to be represented and for different scenarios to be tried out. We thoroughly explore the space of possible strategies both for those unemployed and for companies and analyze the trade-off between these strategies regarding global social and economical indicators.
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Dosi, G., M. C. Pereira, A. Roventini, and M. E. Virgillito. "Technological paradigms, labour creation and destruction in a multi-sector agent-based model." Research Policy 51, no. 10 (December 2022): 104565. http://dx.doi.org/10.1016/j.respol.2022.104565.
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Ygge, F., and H. Akkermans. "Decentralized Markets versus Central Control: A Comparative Study." Journal of Artificial Intelligence Research 11 (October 1, 1999): 301–33. http://dx.doi.org/10.1613/jair.627.
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Multi-Agent Systems (MAS) promise to offer solutions to problems where established, older paradigms fall short. In order to validate such claims that are repeatedly made in software agent publications, empirical in-depth studies of advantages and weaknesses of multi-agent solutions versus conventional ones in practical applications are needed. Climate control in large buildings is one application area where multi-agent systems, and market-oriented programming in particular, have been reported to be very successful, although central control solutions are still the standard practice. We have therefore constructed and implemented a variety of market designs for this problem, as well as different standard control engineering solutions. This article gives a detailed analysis and comparison, so as to learn about differences between standard versus agent approaches, and yielding new insights about benefits and limitations of computational markets. An important outcome is that ``local information plus market communication produces global control''.
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Yang, Shuo, Xinjun Mao, Sen Yang, and Zhe Liu. "Towards a hybrid software architecture and multi-agent approach for autonomous robot software." International Journal of Advanced Robotic Systems 14, no. 4 (July 1, 2017): 172988141771608. http://dx.doi.org/10.1177/1729881417716088.
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To support robust plan execution of autonomous robots in dynamic environments, autonomous robot software should include adaptive and reactive capabilities to cope with the dynamics and uncertainties of the evolving states of real-world environments. However, conventional software architectures such as sense-model-plan-act and behaviour-based paradigms are inadequate for meeting the requirements. A lack of sensing during acting in the sense-model-plan-act paradigm makes the software slow to react to run-time contingencies, whereas the behaviour-based architectures typically fall short in planning of long-range steps and making optimized plan adaptations. This article proposes a hybrid software architecture that maintains both adaptivity and reactivity of robot behaviours in dynamic environments. To implement this architecture, we further present the multi-agent development framework known as AutoRobot, which views the robot software as a multi-agent system in which diverse agent roles collaborate to achieve software functionalities. To demonstrate the applicability and validity of our concrete framework and software architecture, we conduct an experiment to implement a typical case, for example, a robot that autonomously picks up and drops off dishes for remote guests, which requires the robot to plan and navigate in a highly dynamic environment and can adapt its behaviours to unexpected situations.
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Jaswal, Shivani, and Manisha Malhotra. "MATEM." International Journal of Cloud Applications and Computing 12, no. 1 (January 1, 2022): 1–17. http://dx.doi.org/10.4018/ijcac.305213.
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The paradigms of Cloud Computing have risen at a very rapid rate. The Cloud Computing demands that a trustworthy or reliable service should be availed by its potential user. However, it is always a challenge for any cloud user to look for service that is suitable as well as reliable in every aspect. For a trustworthy discovery and delivery of cloud services, a multi-layered model i.e., Multi Agent based Trust Evaluation Model (MATEM) has been proposed. It is a multi-agent trust model that will make use of multiple agents to perform and evaluate the credibility of trust through trust evaluation system. Also, the performance validation has proven that the final calculated values of trust will be helpful in providing reliable cloud services to its users.
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Moummadi, Kamal, Rachida Abidar, and Hicham Medromi. "Distributed Resource Allocation." International Journal of Mobile Computing and Multimedia Communications 4, no. 2 (April 2012): 49–62. http://dx.doi.org/10.4018/jmcmc.2012040104.
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The growth of technological capabilities of mobile devices, the evolution of wireless communication technologies, and the maturity of embedded systems contributed to expand the Machine to machine (M2M) concept. M2M refers to data communication between machines without human intervention. The objective of this paper is to present the grand schemes of a model to be used in an agricultural Decision support System. The authors start by explaining and justifying the need for a hybrid system that uses both Multi-Agent System (MAS) and Constraint Programming (CP) paradigms. Then, the authors propose an approach for Constraint Programming and Multi-Agent System mixing based on controller agent concept. The authors present concrete constraints and agents to be used in a distributed architecture based on the proposed approach for M2M services and agricultural decision support. The platform is built in Java using general interfaces of both MAS and Constraint Satisfaction Problem (CSP) platforms and the conception is made by agent UML (AUML).
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Petrich, Adam M., Joseph A. Sparano, and Samir Parekh. "Paradigms and Controversies in the Treatment of HIV-Related Burkitt Lymphoma." Advances in Hematology 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/403648.
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Burkitt lymphoma (BL) is a very aggressive subtype of non-Hodgkin's lymphoma that occurs with higher frequency in patients with HIV/AIDS. Patients with HIV-related BL (HIV-BL) are usually treated with high-intensity, multi-agent chemotherapy regimens. The addition of the monoclonal antibody Rituximab to chemotherapy has also been studied in this setting. The potential risks and benefits of commonly used regimens are reviewed herein, along with a discussion of controversial issues in the practical management of HIV-BL, including concurrent anti-retroviral therapy, treatment of relapsed and/or refractory disease, and the role of stem cell transplantation.
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Sarratt, Trevor. "Leveraging Communication for Player Modeling and Cooperative Play." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 6 (June 29, 2021): 14–17. http://dx.doi.org/10.1609/aiide.v10i6.12695.
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Collaboration between agents and players within games is a ripe area for exploration. As with adversarial AI, collaborative agents are challenged to accurately model players and adapt their behavior accordingly. The task of cooperation, however, allows for communication between teammates that can prove beneficial in coordinating joint actions and plans. Furthermore, we propose extending established multi-agent communication paradigms to include transfer of information pertinent to player models. By querying goal and preference information from a player, an agent can reduce uncertainty in coordination domains, allowing for more effective planning. We discuss the challenges as well as the planned development and evaluation of the system.
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González-Briones, Alfonso, Fernando De La Prieta, Mohd Mohamad, Sigeru Omatu, and Juan Corchado. "Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review." Energies 11, no. 8 (July 24, 2018): 1928. http://dx.doi.org/10.3390/en11081928.
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This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.
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Mendoza, E., J. Andramuño, J. Núñez, and L. Córdova. "Intelligent multi-agent architecture for a supervisor of a water treatment plant." Journal of Physics: Conference Series 2090, no. 1 (November 1, 2021): 012124. http://dx.doi.org/10.1088/1742-6596/2090/1/012124.
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Abstract The rapid development of Information and Communication Technologies (ICT) and high-capacity hardware components make it necessary to achieve a strong integration of automatic systems based on new paradigms on intelligent distributed architectures, where require highly complex supervision and control tasks, due to the generated requirements of the new production systems, the high number of variables to control and the advancement of technologies, especially in industries where continuous processes have been established. In the present work, a distributed hierarchical modular architecture is proposed for a supervision system, based on multi-agent systems (MAS), oriented to the management of processes in the filtration stage of a water purification plant, using a methodology to the implementation of intelligent agents that allow to project, design, verify and validate the system. This methodology is fundamentally based on the use of the Unified Modeling Language (UML) for its projection and Petri nets (PN) for the simulation and validation of properties, which allows to guarantee the modularity, flexibility, and robustness of the proposed system. The architectures of the intelligent agents in the different programmable devices are modeled and simulated to achieve an adequate interaction and collaboration, allowing to reduce the conflicts that may be generated between them. The evaluation of the distributed architecture focuses on the fulfillment of the functional requirements and evaluation metrics, which, through the analysis of the properties of the Petri net, allows to determine the correct operation of the system and its dynamic behavior in the face of unforeseen situations at different levels of automation.
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Algarvio, Hugo. "Management of Local Citizen Energy Communities and Bilateral Contracting in Multi-Agent Electricity Markets." Smart Cities 4, no. 4 (November 27, 2021): 1437–53. http://dx.doi.org/10.3390/smartcities4040076.
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Over the last few decades, the electricity sector has experienced several changes, resulting in different electricity markets (EMs) models and paradigms. In particular, liberalization has led to the establishment of a wholesale market for electricity generation and a retail market for electricity retailing. In competitive EMs, customers can do the following: freely choose their electricity suppliers; invest in variable renewable energy such as solar photovoltaic; become prosumers; or form local alliances such as Citizen Energy Communities (CECs). Trading of electricity can be done in spot and derivatives markets, or by bilateral contracts. This article focuses on CECs. Specifically, it presents how agent-based local consumers can form alliances as CECs, manage their resources, and trade on EMs. It also presents a review of how agent-based systems can model and support the formation and interaction of alliances in the electricity sector. The CEC can trade electricity directly with sellers through private bilateral agreements. During the negotiation of private bilateral contracts, the CEC receives the prices and volumes of their members and according to its negotiation strategy, tries to satisfy the electricity demands of all members and reduce their costs for electricity.
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Ovalle Carranza, Demetrio Arturo, Jovani Alberto Jiménez Builes, and César Alberto Collazos Ordóñez. "Model for supporting awareness in the CSCL ALLEGRO environment through a blackboard architecture." Ingeniería e Investigación 26, no. 3 (September 1, 2006): 67–77. http://dx.doi.org/10.15446/ing.investig.v26n3.14753.
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This paper is aimed at describing collaborative activities, the awareness process and blackboard architecture used within the ALLEGRO system, a multi-agent learning and teaching environment which has included artificial intelligence mechanisms (instructional planning (IP), cased-based reasoning (CBR), multi-agent systems (MAS), intelligent tutoring systems (ITS) and computer supported collaborative learning (CSCL)). The proposed model of awareness promotes communication, collaboration and coordination in CSCL ALLEGRO. MAS-CommonKADS methodology was applied for building the Multi-Agent system. ALLEGRO based its instructional approach on the following three pedagogical paradigms: conductive behavior, cognitive mechanisms (distributed cognition and problem-based learning) and social-historical theory. The system was validated via several case studies regarding the graphical digital domain for postgraduate architectural studies. The proposed awareness model lets students develop important skills such as conscious behavior, communication, collaboration and coordination in the CSCL component of the ALLEGRO system. This model facilitates students acquiring a perception of what others are doing inside the CSCL, thereby using such knowledge for interacting with the environment and developing leading to expressing and exchanging ideas and opinions with other students, transforming the class into an open virtual forum for reflection, proactive interaction and a critical space for exchanging ideas. The proposed instructional model is a valuable resource from the technological and pedagogical point of view as different theories are integrated, some of them conflicting with others.
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d'AVILA GARCEZ, ARTUR S., LUÍS C. LAMB, KRYSIA BRODA, and DOV M. GABBAY. "APPLYING CONNECTIONIST MODAL LOGICS TO DISTRIBUTED KNOWLEDGE REPRESENTATION PROBLEMS." International Journal on Artificial Intelligence Tools 13, no. 01 (March 2004): 115–39. http://dx.doi.org/10.1142/s0218213004001442.
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Neural-Symbolic Systems concern the integration of the symbolic and connectionist paradigms of Artificial Intelligence. Distributed knowledge representation is traditionally seen under a symbolic perspective. In this paper, we show how neural networks can represent distributed symbolic knowledge, acting as multi-agent systems with learning capability (a key feature of neural networks). We apply the framework of Connectionist Modal Logics to well-known testbeds for distributed knowledge representation formalisms, namely the muddy children and the wise men puzzles. Finally, we sketch a full solution to these problems by extending our approach to deal with knowledge evolution over time.
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Pantazis, Evangelos, and David Gerber. "A framework for generating and evaluating façade designs using a multi-agent system approach." International Journal of Architectural Computing 16, no. 4 (November 28, 2018): 248–70. http://dx.doi.org/10.1177/1478077118805874.
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Digital design paradigms in architecture have been rooted in representational models which are geometry centered and therefore fail to capture building complexity holistically. Due to a lack of computational design methodologies, existing digital design workflows do little in predicting design performance in the early design stage and in most cases analysis and design optimization are done after a design is fixed. This work proposes a new computational design methodology, intended for use in the area of conceptual design of building design. The proposed methodology is implemented into a multi-agent system design toolkit which facilitates the generation of design alternatives using stochastic algorithms and their evaluation using multiple environmental performance metrics. The method allows the user to probabilistically explore the solution space by modeling the design parameters’ architectural design components (i.e. façade panel) into modular programming blocks (agents) which interact in a bottom-up fashion. Different problem requirements (i.e. level of daylight inside a space, openings) described into agents’ behavior allow for the coupling of data from different engineering fields (environmental design, structural design) into the a priori formation of architectural geometry. In the presented design experiment, a façade panel is modeled into an agent-based fashion and the multi-agent system toolkit is used to generate and evolve alternative façade panel configurations based on environmental parameters (daylight, energy consumption). The designer can develop the façade panel geometry, design behaviors, and performance criteria to evaluate the design alternatives. The toolkit relies on modular and functionally specific programming modules (agents), which provide a platform for façade design exploration by combining existing three-dimensional modeling and analysis software.
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Dibley, Michael, Haijiang Li, Yacine Rezgui, and John Miles. "AN INTEGRATED FRAMEWORK UTILISING SOFTWARE AGENT REASONING AND ONTOLOGY MODELS FOR SENSOR BASED BUILDING MONITORING." Journal of Civil Engineering and Management 21, no. 3 (February 26, 2015): 356–75. http://dx.doi.org/10.3846/13923730.2014.890645.
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Smart building monitoring demands a new software infrastructure that can elaborate building domain knowledge in order to provide advanced and intelligent functionalities. Conventional facility management (FM) software tools lack semantically rich components, and that limits the capability of supporting software for automatic information sharing, resource negotiation and to assist in timely decision making. Recent hardware innovation on compact ZigBee sensor devices, software developments on ontology and intelligent software agent paradigms provide a good opportunity to develop tools that can further improve current FM practices. This paper introduces an integrated framework which includes a ZigBee based sensor network and underlying multi-agent software (MAS) components. Several different types of sensors were integrated with the ZigBee host devices to produce compact multi-functional sensor units. The MAS framework incorporates the belief-desire-intention (BDI) abstraction with ontology support (provided via explicit knowledge bases). The different software agent types have been developed to work with sensor hardware to conduct resource negotiation, to optimize battery utilization, to monitor building space in a non-intrusive way and to reason about its usage through real time ontology model queries. The deployed sensor network shows promising intelligent characteristics, and it has been applied in several on-going research projects as an underlying decision making service. More applications and larger deployments have been planned for future work.
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Casadei, Roberto, Gianluca Aguzzi, and Mirko Viroli. "A Programming Approach to Collective Autonomy." Journal of Sensor and Actuator Networks 10, no. 2 (April 19, 2021): 27. http://dx.doi.org/10.3390/jsan10020027.
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Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.
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Bertoli, Annalisa, Andrea Cervo, Carlo Alberto Rosati, and Cesare Fantuzzi. "Smart Node Networks Orchestration: A New E2E Approach for Analysis and Design for Agile 4.0 Implementation." Sensors 21, no. 5 (February 26, 2021): 1624. http://dx.doi.org/10.3390/s21051624.
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The field of cyber-physical systems is a growing IT research area that addresses the deep integration of computing, communication and process control, possibly with humans in the loop. The goal of such area is to define modelling, controlling and programming methodologies for designing and managing complex mechatronics systems, also called industrial agents. Our research topic mainly focuses on the area of data mining and analysis by means of multi-agent orchestration of intelligent sensor nodes using internet protocols, providing also web-based HMI visualizations for data interpretability and analysis. Thanks to the rapid spreading of IoT systems, supported by modern and efficient telecommunication infrastructures and new decentralized control paradigms, the field of service-oriented programming finds new application in wireless sensor networks and microservices paradigm: we adopted such paradigm in the implementation of two different industrial use cases. Indeed, we expect a concrete and deep use of such technologies with 5G spreading. In the article, we describe the common software architectural pattern in IoT applications we used for the distributed smart sensors, providing also design and implementation details. In the use case section, the prototypes developed as proof of concept and the KPIs used for the system validation are described to provide a concrete solution overview.
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Hu, Chen, and James J. Dignam. "Biomarker-Driven Oncology Clinical Trials: Key Design Elements, Types, Features, and Practical Considerations." JCO Precision Oncology, no. 3 (December 2019): 1–12. http://dx.doi.org/10.1200/po.19.00086.
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In this precision oncology era, where molecular profiling at the individual patient level becomes increasingly accessible and affordable, more and more clinical trials are now driven by biomarkers, with an overarching objective to optimize and personalize disease management. As compared with the conventional clinical development paradigms, where the key is to evaluate treatment effects in histology-defined populations, the choices of biomarker-driven clinical trial designs and analysis plans require additional considerations that are heavily dependent on the nature of biomarkers (eg, prognostic or predictive, integral or integrated) and the credential of biomarkers’ performance and clinical utility. Most recently, another major paradigm change in biomarker-driven trials is to conduct multi-agent and/or multihistology master protocols or platform trials. These trials, although they may enjoy substantial infrastructure and logistical advantages, also face unique operational and conduct challenges. Here we provide a concise overview of design options for both the setting of single-biomarker/single-disease and the setting of multiple-biomarker/multiple-disease types. We focus on explaining the trial design and practical considerations and rationale of when to use which designs, as well as how to incorporate various adaptive design components to provide additional flexibility, enhance logistical efficiency, and optimize resource allocation. Lessons learned from real trials are also presented for illustration.
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Veiga, Jackson T., Marcosiris A. O. Pessoa, Fabrício Junqueira, Paulo E. Miyagi, and Diolino J. dos Santos Filho. "A Systematic Modelling Procedure to Design Agent-Oriented Control to Coalition of Capabilities—In the Context of I4.0 as Virtual Assets (AAS)." Computers 10, no. 12 (November 28, 2021): 161. http://dx.doi.org/10.3390/computers10120161.
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Manufacturing systems need to meet Industry 4.0 (I4.0) guidelines to deal with uncertainty in scenarios of turbulent demand for products. The engineering concepts to define the service’s resources to manufacture the products will be more flexible, ensuring the possibility of re-planning in operation. These can follow the engineering paradigm based on capabilities. The virtualization of industry components and assets achieves the RAMI 4.0 guidelines and (I4.0C), which describes the Asset Administration Shell (AAS). However, AAS are passive components that provide information about I4.0 assets. The proposal of specific paradigms is exposed for managing these components, as is the case of multi-agent systems (MAS) that attribute intelligence to objects. The implementation of resource coalitions with evolutionary architectures (EAS) applies cooperation and capabilities’ association. Therefore, this work focuses on designing a method for modeling the asset administration shell (AAS) as virtual elements orchestrating intelligent agents (MAS) that attribute cooperation and negotiation through contracts to coalitions based on the engineering capabilities concept. The systematic method suggested in this work is partitioned for the composition of objects, AAS elements, and activities that guarantee the relationship between entities. Finally, Production Flow Schema (PFS) refinements are applied to generate the final Petri net models (PN) and validate them with Snoopy simulations. The results achieved demonstrate the validation of the procedure, eliminating interlocking and enabling liveliness to integrate elements’ behavior.
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Dushkin, R. V., V. A. Lelecova, and K. Yu Eidemiller. "System of Operations on Associative Heterarchical Memory." Programmnaya Ingeneria 14, no. 2 (February 8, 2023): 69–76. http://dx.doi.org/10.17587/prin.14.69-76.
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Text processing in natural language remains an important task for the field of development of artificial intelligence methods and tools. Since the twentieth century, artificial intelligence methods have been divided into two paradigms — top-down and bottom-up. The methods of the ascending paradigm are difficult to interpret in the form of the output of natural language, and the methods of the descending paradigm are difficult to actualize information. Taking into account the authors approach to the construction of artificial intelligence agents, the processing of natural language must be performed on two levels: on the lower level, using methods of the bottom-up paradigm, and on the upper level, using symbolic methods of the top-down paradigm. The authors of the article have already introduced a new mathematical formalism based on the notion of a hypergraph — associative heterarchical memory (AH-memory). Such memory should simplify the process of natural language processing with new technologies. Earlier the authors group has thoroughly analyzed the problem of symbol binding in the application to АН-memory and its structure. In the first paper, abstract symbol binding was performed using multi-serial integration, eventually converting the primary symbols received by the program into integrated abstract symbols. The second paper provided a comprehensive description of the AH-memory in the form of formulas, explanations of them, and their corresponding diagrams. Although there are many possible modules to use, the developer working with AH-memory should choose those parts of AH-memory which are required for successful and efficient functioning of the AI agent. The article will be of interest to developers of artificial intelligence methods and tools, mathematicians and specialists in natural language processing.
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Ezell, Barry, Christopher Lynch, and Patrick Hester. "Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques." Applied Sciences 11, no. 21 (November 5, 2021): 10397. http://dx.doi.org/10.3390/app112110397.
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Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions.
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Dushkin, R. V., V. A. Lelekova, V. Y. Stepankov, and S. Fadeeva. "Structure of associative heterarchical memory." Russian Technological Journal 10, no. 5 (October 20, 2022): 7–15. http://dx.doi.org/10.32362/2500-316x-2022-10-5-7-15.
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Objectives. Since the 20th century, artificial intelligence methods can be divided into two paradigms: top-down and bottom-up. While the methods of the ascending paradigm are difficult to interpret as natural language outputs, those applied according to the descending paradigm make it difficult to actualize information. Thus, natural language processing (NLP) by artificial intelligence remains a pressing problem of our time. The main task of NLP is to create applications that can process and understand natural languages. According to the presented approach to the construction of artificial intelligence agents (AI-agents), processing of natural language should be conducted at two levels: at the bottom, methods of the ascending paradigm are employed, while symbolic methods associated with the descending paradigm are used at the top. To solve these problems, the authors of the present paper propose a new mathematical formalism: associative heterarchical memory (AH-memory), whose structure and functionality are based both on bionic principles and on the achievements of top-down and bottom-up artificial intelligence paradigms.Methods. Natural language recognition algorithms were used in conjunction with various artificial intelligence methods.Results. The problem of character binding as applied to AH-memory was explored by the research group in earlier research. Here, abstract symbol binding was performed using multi-serial integration, eventually converting the primary symbols produced by the program into integrated abstract symbols. The present paper provides a comprehensive description of AH-memory in the form of formulas, along with their explanations and corresponding schemes.Conclusions. The most universal structure of AH-memory is presented. When working with AH-memory, a developer should select from a variety of possible module sets those AH-memory components that support the most successful and efficient functioning of the AI-agent.
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Gong, Tao, Jia Jia Zhou, and Lei Qi. "Intelligent Techniques in Teaching Science of Artificial Immune System." Applied Mechanics and Materials 48-49 (February 2011): 637–40. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.637.
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Building on three theoretical paradigms (student model, ICAI model, and multi-dimension education immune agent), some intelligent techniques are proposed and designed to teach Fuzzy Mathematics and Science of Artificial Immune System in a web-based way. The goal of the teaching methodology is a new learning, which is interactive, sharing, open, cooperative, and autonomous. The great difference between traditional approaches for teaching such knowledge and the new approach in this paper is the centre of teaching. The traditional teaching is centered with teachers but the new teaching is centered with students. The teaching system for Fuzzy Mathematics and Science of Artificial Immune System is a virtual classroom based on the web, and the two courses are designed as web-based courses. Moreover, for Science of Artificial Immune System, the web-based course system is a typical artificial immune system in fact, and students can learn more real knowledge from the web-based course immune system.
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Kazakov, Alexander, Anna Lempert, and Alexander Stolbov. "On technology for modeling and forecasting the interrelated development of regional fuel and energy complexes of Russia and Mongolia." E3S Web of Conferences 209 (2020): 07019. http://dx.doi.org/10.1051/e3sconf/202020907019.
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The paper considers the main ideas of proposed computational technology for scenario modeling and forecasting the development of the national fuel and energy complexes of Russia and Mongolia, regarding to the intercountry trade in fuel and energy resources. The proposing technology exploits the ideas of multi-agent systems (MASs) and agent-based simulation models (ABSMs) as they can act as unifying means for different types of decision-making methods by distributed objects. Methodological principles and architecture for ABSM of the national fuel and energy complexes of Russia and Mongolia were proposed. The implementation issues of the model in the Adskit software tool is discussed. The problem of laying routes of extended energy objects is also considered. Based on the principles of geometric optics the author’s solution algorithm in the form of a special case of the variational problem was proposed to deal with this problem. The application of MAS and ABSM paradigms for forecasting and evaluating the state of the fuel and energy sector in Russia and Mongolia allows one to organize a step-by-step research of the energy system with the gradual development of the model: increasing the types of objects and agents; improving decision-making algorithms, including those based on mathematical models; creating complex scenarios. The technology forms methodological basis for supporting decision-making process of evaluation the prospective variants of bilateral energy cooperation of Russia and Mongolia and related project effectiveness.
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Klener Jr, Pavel, Tomas Etrych, and Pavel Klener. "Biological Therapy of Hematologic Malignancies: Toward a Chemotherapy- free Era." Current Medicinal Chemistry 26, no. 6 (May 13, 2019): 1002–18. http://dx.doi.org/10.2174/0929867324666171006144725.
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:Less than 70 years ago, the vast majority of hematologic malignancies were untreatable diseases with fatal prognoses. The development of modern chemotherapy agents, which had begun after the Second World War, was markedly accelerated by the discovery of the structure of DNA and its role in cancer biology and tumor cell division. The path travelled from the first temporary remissions observed in children with acute lymphoblastic leukemia treated with single-agent antimetabolites until the first cures achieved by multi-agent chemotherapy regimens was incredibly short. Despite great successes, however, conventional genotoxic cytostatics suffered from an inherently narrow therapeutic index and extensive toxicity, which in many instances limited their clinical utilization. In the last decade of the 20th century, increasing knowledge on the biology of certain malignancies resulted in the conception and development of first molecularly targeted agents designed to inhibit specific druggable molecules involved in the survival of cancer cells. Advances in technology and genetic engineering enabled the production of structurally complex anticancer macromolecules called biologicals, including therapeutic monoclonal antibodies, antibody-drug conjugates and antibody fragments. The development of drug delivery systems (DDSs), in which conventional drugs were attached to various types of carriers including nanoparticles, liposomes or biodegradable polymers, represented an alternative approach to the development of new anticancer agents. Despite the fact that the antitumor activity of drugs attached to DDSs was not fundamentally different, the improved pharmacokinetic profiles, decreased toxic side effects and significantly increased therapeutic indexes resulted in their enhanced antitumor efficacy compared to conventional (unbound) drugs.:Approval of the first immune checkpoint inhibitor for the treatment of cancer in 2011 initiated the era of cancer immunotherapy. Checkpoint inhibitors, bispecific T-cell engagers, adoptive T-cell approaches and cancer vaccines have joined the platform so far, represented mainly by recombinant cytokines, therapeutic monoclonal antibodies and immunomodulatory agents. In specific clinical indications, conventional drugs have already been supplanted by multi-agent, chemotherapy-free regimens comprising diverse immunotherapy and/or targeted agents. The very distinct mechanisms of the anticancer activity of new immunotherapy approaches not only call for novel response criteria, but might also change fundamental treatment paradigms of certain types of hematologic malignancies in the near future.
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De Turck, F., S. Vanhaste, F. Vandermeulen, P. Demeester, G. de Moor, and J. Decruyenaere. "On the Design of a Generic and Scalable Multilayer Software Architecture for Data Flow Management in the Intensive Care Unit." Methods of Information in Medicine 42, no. 01 (2003): 79–88. http://dx.doi.org/10.1055/s-0038-1634212.
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Summary Objectives: The current Intensive Care Information Systems (IC-ISs) collect and store monitoring data in an automated way and can replace all paper forms by an electronic equivalent, resulting in a paperless ICU. Future development of IC-ISs will now have to focus on bedside clinical decision support. The current IC-ISs are data-driven systems, with a two-layer software architecture. This software architecture is hardly maintainable and probably not the most optimal architecture to make the transition towards future systems with decision support. The aim of this research was to address the design of an alternative software architecture based on new paradigms. Methods: State-of-the art component, middleware and agent technology were deployed to design and implement a software architecture for ICU data flow management. Results: An advanced multi-layer architecture for efficient data flow management in the ICU has been designed. The architecture is both generic and scalable, which means that it neither depends on a particular ICU nor on the deployed monitoring devices. Automatic device detection and Graphical User Interface generation are taken into account. Furthermore, a demonstrator has been developed as a proof that the proposed conceptual software architecture is feasible in practice. The core of the new architecture consists of Bed Decision Agents (BDAs). The introduction of BDAs, who perform specific dedicated tasks, improves the adaptability and maintainability of the future very complex IC-ISs. Conclusions: A software architecture, based on component, middleware and agent technology, is feasible and offers important advantages over the currently used two-layer software architecture.
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Dhakal, Binod, Saulius Girnius, and Parameswaran Hari. "Recent advances in understanding multiple myeloma." F1000Research 5 (August 23, 2016): 2053. http://dx.doi.org/10.12688/f1000research.8777.1.
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There have been major recent advancements in the understanding and management of multiple myeloma. Diagnostic criteria have been revised and former ultra-high-risk smoldering multiple myeloma is now considered multiple myeloma in need of treatment. Understanding clonal progression, evolution, and tides not only has helped elucidate the disease behavior but might help expand therapeutic choices in order to select appropriate treatment for patients. Unprecedented response rates with modern triplet induction therapies containing proteasome inhibitor and immunomodulators have made this approach standard for initial treatment. The US Food and Drug Administration approved four new drugs (two targeted antibodies and two oral agents) in 2015 in relapsed/refractory multiple myeloma and these drugs along with the other already-available drugs have now increased the choices of regimens. Even drugs without single-agent activity, such as panobinostat and elotuzumab, have an important role, especially in the proteasome inhibitor refractory setting. Recent studies done in the context of novel agent induction suggest that high-dose therapy followed by autologous transplant continues to improve response rates and progression-free survival, thus underscoring their role in transplant-eligible patients. Evolving paradigms in the treatment of multiple myeloma include newer promising immune approaches, such as adoptive cellular therapies, vaccines, or antibody-based immune manipulations. Though multiple myeloma is still considered incurable, it is clear that with the improved understanding of disease biology and clonal architecture of relapse combined with the availability of multi-targeted approaches, we are ever closer to a lasting cure or transformation into indolent and long-lasting disease courses or both.
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McDonald, Kelsey R., John M. Pearson, and Scott A. Huettel. "Dorsolateral and dorsomedial prefrontal cortex track distinct properties of dynamic social behavior." Social Cognitive and Affective Neuroscience 15, no. 4 (April 2020): 383–93. http://dx.doi.org/10.1093/scan/nsaa053.
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Abstract Understanding how humans make competitive decisions in complex environments is a key goal of decision neuroscience. Typical experimental paradigms constrain behavioral complexity (e.g. choices in discrete-play games), and thus, the underlying neural mechanisms of dynamic social interactions remain incompletely understood. Here, we collected fMRI data while humans played a competitive real-time video game against both human and computer opponents, and then, we used Bayesian non-parametric methods to link behavior to neural mechanisms. Two key cognitive processes characterized behavior in our task: (i) the coupling of one’s actions to another’s actions (i.e. opponent sensitivity) and (ii) the advantageous timing of a given strategic action. We found that the dorsolateral prefrontal cortex displayed selective activation when the subject’s actions were highly sensitive to the opponent’s actions, whereas activation in the dorsomedial prefrontal cortex increased proportionally to the advantageous timing of actions to defeat one’s opponent. Moreover, the temporoparietal junction tracked both of these behavioral quantities as well as opponent social identity, indicating a more general role in monitoring other social agents. These results suggest that brain regions that are frequently implicated in social cognition and value-based decision-making also contribute to the strategic tracking of the value of social actions in dynamic, multi-agent contexts.
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Leiser, Owen P., Errett C. Hobbs, Amy C. Sims, George W. Korch, and Karen L. Taylor. "Beyond the List: Bioagent-Agnostic Signatures Could Enable a More Flexible and Resilient Biodefense Posture Than an Approach Based on Priority Agent Lists Alone." Pathogens 10, no. 11 (November 17, 2021): 1497. http://dx.doi.org/10.3390/pathogens10111497.
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As of 2021, the biothreat policy and research communities organize their efforts around lists of priority agents, which elides consideration of novel pathogens and biotoxins. For example, the Select Agents and Toxins list is composed of agents that historic biological warfare programs had weaponized or that have previously caused great harm during natural outbreaks. Similarly, lists of priority agents promulgated by the World Health Organization and the National Institute of Allergy and Infectious Diseases are composed of previously known pathogens and biotoxins. To fill this gap, we argue that the research/scientific and biodefense/biosecurity communities should categorize agents based on how they impact their hosts to augment current list-based paradigms. Specifically, we propose integrating the results of multi-omics studies to identify bioagent-agnostic signatures (BASs) of disease—namely, patterns of biomarkers that accurately and reproducibly predict the impacts of infection or intoxication without prior knowledge of the causative agent. Here, we highlight three pathways that investigators might exploit as sources of signals to construct BASs and their applicability to this framework. The research community will need to forge robust interdisciplinary teams to surmount substantial experimental, technical, and data analytic challenges that stand in the way of our long-term vision. However, if successful, our functionality-based BAS model could present a means to more effectively surveil for and treat known and novel agents alike.
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Montero, Luis, Antonio Bello, and Javier Reneses. "A New Methodology to Obtain a Feasible Thermal Operation in Power Systems in a Medium-Term Horizon." Energies 13, no. 12 (June 12, 2020): 3056. http://dx.doi.org/10.3390/en13123056.
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Nowadays, electricity market paradigms are constantly changing. On the one hand, the deployment of non-dispatchable renewable energy sources is bringing out the necessity of representing hourly dynamics in medium-term fundamental models. On the other, the promotion of new interconnection capacity and the integration of markets (as is the case of the European market) makes necessary the simultaneous modeling of multiple electricity systems. Thus, the large size of power markets, together with the consideration of uncertainty in some inputs, make it computationally intractable to work rigorously on an hourly detailed time span. Temporal aggregation, integer programming relaxation or less accurate generation modeling are usually employed to obtain reasonable computation times. However, the application of these techniques often leads to infeasible or suboptimal operational outputs. This paper proposes a new soft-linking methodology to meet reliable results from medium-term models, such as hourly prices or aggregated productions, with a feasible and detailed representation of the thermal generation, considering technical constraints and risk aversion. The results of a fundamental model that represents the competitive behavior between market players in a multi-area power system are used as the starting point for the methodology. Then, a post-processing method is applied to optimize and make feasible the thermal portfolio of a market agent. The final output is a feasible hourly scheduling and an ample space for optimization, where the introduction of a strategic term represents the rational behavior of a player who tries to maximize its profit.
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Fathi, Amir T., Daniel J. DeAngelo, Kristen E. Stevenson, Jonathan E. Kolitz, Julie D. Asch, Philip C. Amrein, Eyal C. Attar, et al. "Intensified Chemotherapy for Older Patients with Acute Lymphoblastic Leukemia (ALL): A Phase II Study from the Dana Farber Cancer Institute (DFCI) ALL Consortium." Blood 124, no. 21 (December 6, 2014): 3714. http://dx.doi.org/10.1182/blood.v124.21.3714.3714.
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Abstract Unlike the significant advances seen with intensive chemotherapy for pediatric acute lymphoblastic leukemia (ALL) over the last two decades, long-term outcomes among patients over age 50 remain poor, with median survival less than one year. This contrast has been attributed to high-risk chromosomal features, decreased compliance with and tolerance of effective therapies, and exposure to less intensive multi-agent regimens among adults with ALL. In recent years, more intensive chemotherapeutic paradigms, derived from pediatric protocols, have been studied in adult ALL. The purpose of the current study was to determine the efficacy of an intensified multi-agent approach, derived from a completed DFCI consortium pediatric regimen used in younger adults, in an older (age >50) population of patients with ALL. For this study, modifications of the pediatric regimen included incorporation of clofarabine in consolidation, adjustment to dose and scheduling of PEG asparaginase and steroids, as well as inclusion of stem cell transplant (SCT) for eligible patients. The primary endpoint was survival rate at 1 year, with the goal of improving from the 33% historical control to 53%. Adults, aged 51-75 years, with newly diagnosed ALL or lymphoblastic lymphoma, were eligible. During induction, patients received multi-agent chemotherapy with vincristine, prednisone, doxorubicin, and PEG asparaginase. Imatinib was instituted if cytogenetics confirmed the presence of the Philadelphia chromosome. Patients received prophylactic intrathecal therapy with induction, and those with CNS involvement underwent additional IT therapy. Prednisone was administered for 21 days for those aged less than 60 and for 7 days for those aged 60 and above. Following induction, cycle one of consolidation included treatment with clofarabine, prednisone, and PEG asparaginase. After induction and first consolidation course, eligible patients proceeded to allogeneic SCT. Patients without matched sibling donors could receive unrelated donor or cord blood transplants. While those eligible under age 60 could undergo ablative conditioning regimens, those 60 and older received reduced intensity regimens. Those not eligible for SCT, went on to receive CNS, consolidation and continuation phases of treatment, as per protocol, which incorporated treatment with cycles of vincristine, doxorubicin, 6-mercaptopurine, and dexamethasone. PEG asparaginase was incorporated into the induction, consolidation I, CNS phase, and consolidation II phases of therapy. As of the most current analysis, 30 patients have been enrolled. A total of 19 of 29 evaluable patients (66%) have achieved a complete remission (CR). Three patients were refractory to induction therapy, four discontinued treatment during induction due to toxicity, of which three died, and nine patients have experienced relapse following remission. Nine patients have undergone SCT. A total of 15 patients have died on study out of 27 evaluated, and the overall survival, calculated by the method of Kaplan and Meier, at one year was 62% [95% CI, 41%-77%] (Figure 1), while disease-free survival for the 18 patients who achieved a CR following induction therapy at one year was 77% [95% CI, 49%-90%]. In total, for evaluable patients with at least one year of follow-up, the proportion surviving at one year was 61% [two-sided 80% CI, 47-75%] (16/26), significantly higher than the historical rate (33% used for this analysis, one-sided 90% exact CI) among such patients. Overall survival is also shown for Ph+ and Ph- groups (Figure 2). The most common grade 3/4 toxicities included transaminitis and hyperbilirubinemia, cytopenias, hypophosphatemia, hyperglycemia, and neutropenic fever. The major toxicity of liver injury, thought related to PEG asparaginase, prompted an amendment to the protocol to reduce the dose. Additionally, PEG asparaginase administration was limited to only those with Philadelphia chromosome-negative disease, to decrease risk of severe hepatotoxcity in patients receiving concurrent imatinib and PEG asparaginase. These data suggest that intensive multi-agent chemotherapy is tolerable in older patients with ALL, and can result in improved outcomes when compared to historical data. Additional study of similarly intensive regimens, incorporating novel therapies and alternative formulations of asparaginase, are warranted in older populations of ALL. Disclosures Fathi: Seattle Genetics: Research Funding; Seattle Genetics: Advisory Board, Advisory Board Other. Stone:Novartis: Research Funding.
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Ahmadi, Abbas, Mehrdad Roghani, Sanaz Noori, and Babak Nahri-Niknafs. "Substituted Aminobenzothiazole Derivatives of Tacrine: Synthesis and Study on Learning and Memory Impairment in Scopolamine-Induced Model of Amnesia in Rat." Mini-Reviews in Medicinal Chemistry 19, no. 1 (December 6, 2018): 72–78. http://dx.doi.org/10.2174/1389557518666180716122608.
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Background: Currently, there is no conclusive cure for Alzheimer’s disease (AD) and existing treatments mainly offer symptomatic relief. Dysfunction of the cholinergic system plays an important role in the pathogenesis of AD. Tacrine (1, 2, 3, 4-tetrahydroacridin-9-amine, III) was the first approved agent for the palliative therapy of AD but its use is associated with some complications. Development of novel multi target derivatives of Tacrine with lower complications is strongly warranted. In this study, new aminobenzothiazole (1-5, with many useful biological and pharmacological properties) analogues (IV-VIII) were synthesized by changing of amine moiety of III. Then, the effects of these new compounds on learning and memory impairment in scopolamine-induced model of amnesia were studied and the outcomes were compared with control and Tacrine groups in rat. Material and Methods: The rats received Tacrine or its derivatives (IV-VIII) i.p. for two weeks at a dose of 10 mg/kg. For induction of amnesia, scopolamine at a dose of 1 mg/kg was daily administered i.p. started on day-8 till the end of the study. Behavioral experiments including Y-maze, novel object recognition (discrimination) and passive avoidance paradigms were conducted at week 2. Results: Data analysis showed that some Tacrine derivatives, especially VII with 2-amino, 6-nitrobenzothiazole moiety, could markedly and significantly improve alternation score, discrimination ratio and step through latency compared to control and Tacrine groups. Conclusion: These findings indicated that some of these derivatives (especially compounds VI and VII) are capable to mitigate learning and memory deficits in scopolamine-induced model of amnesia in rats and may have potential benefit in management of patients with AD.
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Mazumdar, Bireshwar Dass, Swati Basak, and Neelam Modanwal. "Multi-Agent Negotiation Paradigm for Agent Selection in B2C E-Commerce." International Journal of Agent Technologies and Systems 3, no. 2 (April 2011): 33–52. http://dx.doi.org/10.4018/jats.2011040103.
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Multi agent system (MAS) model has been extensively used in the different tasks of E-Commerce such as customer relation management (CRM), negotiation and brokering. The objective of this paper is to evaluate a seller agent’s various cognitive parameters like capability, trust, and desire. After selecting a best seller agent from ordering queue, it applies negotiation strategies to find the most profitable proposal for both buyer and seller. This mechanism belongs to a semi cooperative negotiation type, and selecting a seller and buyer agent pair using mental and cognitive parameters. This work provides a logical cognitive model, logical negotiation model between buyer agent and selected seller agent.
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Nordal, Helge, and Idriss El-Thalji. "Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study." Applied Sciences 11, no. 8 (April 13, 2021): 3487. http://dx.doi.org/10.3390/app11083487.
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The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.
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Amine, Khaldi. "THE MULTI-AGENT PARADIGM OF RANGE IMAGE SEGMENTATION." Scientific Visualization 9, no. 5 (December 24, 2017): 38–53. http://dx.doi.org/10.26583/sv.9.5.03.
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Husáková, Martina. "Use of the Multi-Agent Paradigm in Sustainable Tourism." Czech Journal of Tourism 7, no. 1 (June 1, 2018): 5–24. http://dx.doi.org/10.1515/cjot-2018-0001.
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Abstract Complex systems are characterised by a huge amount of components, which are highly linked with each other. Tourism is one of the examples of complex systems collecting various activities leading to the enrichment of travellers in the view of receiving new experiences and increasing economic prosperity of specific destinations. The complex systems can be investigated with various bottom-up and top-down approaches. The multi-agent-based modelling is the bottom-up approach that is focused on the representation of individual entities for the exploration of possible interactions among them and their effects on surrounding environments. These systems are able to integrate knowledge of socio-cultural, economic, physical, biological or environmental systems for in-silico models development, which can be used for experimentation with a system. The main aim of the presented text is to introduce links between tourism, complexity and to advocate usefulness of the multi-agent-based systems for the exploration of tourism and its sustainability. The evaluation of suitability of the multi-agent systems in tourism is based on the investigation of fundamental characteristics of these two systems and on the review of specific applications of the multi-agent systems in sustainable tourism.
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Kissoum, Yacine, and Mohammed Redjimi. "Multi-Level Testing Approach for Multi-Agent Systems." International Journal of Organizational and Collective Intelligence 12, no. 1 (January 1, 2022): 1–23. http://dx.doi.org/10.4018/ijoci.304883.
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The software development cycles need product testing. There is a crucial lack in testing phases of multi-agent systems. To this end, a call for an investigation of appropriate testing techniques is necessary to provide adequate software development processes and supporting tools. Among all existing solutions of the test, the model-based testing technique –MBT) has gained attention with the popularization of models both in software design and development. This technique uses a so-called abstract test model to generate abstract test cases. After their concretization, concrete test cases are submitted to the system under test. The systems outputs are finally compared to the abstract test model expected results. In this context, a model-based testing approach for multi-agent systems based on the Reference net paradigm is proposed in this paper. A running example supported by a multi-agent testing prototype, which aims at simplifying and providing a uniform and automated way for multi-agent systems testing is presented and discussed.
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Penner, Robin R. "Multi-Agent Societies for Collaborative Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 15 (October 1996): 762–66. http://dx.doi.org/10.1177/154193129604001503.
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The application of a multi-agent architecture to the design and operation of automated process management systems is proving to be a fruitful method of facilitating human-system collaboration. The agent architecture we are developing is intended to be applied in environments where humans and automated systems jointly perform information intensive tasks, and is based on an organization of multiple agents, where both human and software agents are integrated members in groups akin to human societies. Important features of our architecture include an organization based on social structures, a user interface model based on a collaborative interaction metaphors, and a situated action paradigm for agent behavior.
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Oliveira, Eugénio, R. Camacho, and C. Ramos. "A multi-agent environment in robotics." Robotica 9, no. 4 (December 1991): 431–40. http://dx.doi.org/10.1017/s0263574700000618.
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SUMMARYThe use of Multi-Agent Systems as a Distributed AI paradigm for Robotics is the principal aim of our present work. In this paper we consider the needed concepts and a suitable architecture for a set of Agents in order to make it possible for them to cooperate in solving non-trivial tasks.Agents are sets of different software modules, each one implementing a function required for cooperation. A Monitor, an Acquaintance and Self-knowledge Modules, an Agenda and an Input queue, on the top of each Intelligent System, are fundamental modules that guarantee the process of cooperation, while the overall aim is devoted to the community of cooperative Agents. These Agents, which our testbed concerns, include Vision, Planner, World Model and the Robot itself.
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Kaihara, T., S. Fujii, and K. Iwata. "Virtual Enterprise Coalition Strategy with Game Theoretic Multi-Agent Paradigm." CIRP Annals 55, no. 1 (2006): 513–16. http://dx.doi.org/10.1016/s0007-8506(07)60471-5.
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Simões, David, Nuno Lau, and Luís Paulo Reis. "Multi Agent Deep Learning with Cooperative Communication." Journal of Artificial Intelligence and Soft Computing Research 10, no. 3 (July 1, 2020): 189–207. http://dx.doi.org/10.2478/jaiscr-2020-0013.
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AbstractWe consider the problem of multi agents cooperating in a partially-observable environment. Agents must learn to coordinate and share relevant information to solve the tasks successfully. This article describes Asynchronous Advantage Actor-Critic with Communication (A3C2), an end-to-end differentiable approach where agents learn policies and communication protocols simultaneously. A3C2 uses a centralized learning, distributed execution paradigm, supports independent agents, dynamic team sizes, partially-observable environments, and noisy communications. We compare and show that A3C2 outperforms other state-of-the-art proposals in multiple environments.
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Su, Jianyu, Stephen Adams, and Peter Beling. "Value-Decomposition Multi-Agent Actor-Critics." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11352–60. http://dx.doi.org/10.1609/aaai.v35i13.17353.
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The exploitation of extra state information has been an active research area in multi-agent reinforcement learning (MARL). QMIX represents the joint action-value using a non-negative function approximator and achieves the best performance on the StarCraft II micromanagement testbed, a common MARL benchmark. However, our experiments demonstrate that, in some cases, QMIX performs sub-optimally with the A2C framework, a training paradigm that promotes algorithm training efficiency. To obtain a reasonable trade-off between training efficiency and algorithm performance, we extend value-decomposition to actor-critic methods that are compatible with A2C and propose a novel actor-critic framework, value-decomposition actor-critic (VDAC). We evaluate VDAC on the StarCraft II micromanagement task and demonstrate that the proposed framework improves median performance over other actor-critic methods. Furthermore, we use a set of ablation experiments to identify the key factors that contribute to the performance of VDAC.
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Kishore, Rajiv, Hong Zhang, and R. Ramesh. "Enterprise integration using the agent paradigm: foundations of multi-agent-based integrative business information systems." Decision Support Systems 42, no. 1 (October 2006): 48–78. http://dx.doi.org/10.1016/j.dss.2004.09.011.
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Kim, Hong-Keun, Seong-Jun Kim, Hyung-Bo Shim, and Ju-Hoon Back. "Order Reduction Paradigm for Consensus of Neutrally Stable Multi-Agent Systems." Journal of Institute of Control, Robotics and Systems 16, no. 3 (March 1, 2010): 222–26. http://dx.doi.org/10.5302/j.icros.2010.16.3.222.
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