Journal articles: 'Design Agent'

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Flathmann, Christopher, Nathan McNeese, and Lorenzo Barberis Canonico. "Using Human-Agent Teams to Purposefully Design Multi-Agent Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1425–29. http://dx.doi.org/10.1177/1071181319631238.

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With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams.

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Lavendelis, Egons, and Janis Grundspenkis. "Design of Multi-Agent Based Intelligent Tutoring Systems." Scientific Journal of Riga Technical University. Computer Sciences 38, no. 38 (January 1, 2009): 48–59. http://dx.doi.org/10.2478/v10143-009-0004-z.

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Design of Multi-Agent Based Intelligent Tutoring SystemsResearch of two fields, namely agent oriented software engineering and intelligent tutoring systems, have to be taken into consideration, during the design of multi-agent based intelligent tutoring systems (ITS). Thus there is a need for specific approaches for agent based ITS design, which take into consideration main ideas from both fields. In this paper we propose a top down design approach for multi-agent based ITSs. The proposed design approach consists of the two main stages: external design and internal design of agents. During the external design phase the behaviour of agents and interactions among them are designed. The following steps are done: task modelling and task allocation to agents, use case map creation, agent interaction design, ontology creation and holon design. During the external design phase agents and holons are defined according to the holonic multi-agent architecture for ITS development. During the internal design stage the internal structure of agents is specified. The internal structure of each agent is represented in the specific diagram, called internal view of the agent, consisting of agent's actions and interactions among them, rules for incoming message and perception processing, incoming and outgoing messages, and beliefs of the agent. The proposed approach is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The approach is developed using the same concepts as JADE agent platform and is suitable for agent code generation from the design diagrams.

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SAUNDERS, ROB, and JOHN S. GERO. "Curious agents and situated design evaluations." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 2 (May 2004): 153–61. http://dx.doi.org/10.1017/s0890060404040119.

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This paper presents a possible future direction for agent-based simulation using complex agents that can learn from experience and report their individual evaluations. Adding learning to the agent model permits the simulation of potentially important agent behavior such as curiosity. The agents can then report evaluations of a design that are situated in their individual experience. The paper describes the architecture of curious agents used in the situated evaluation of designs. It then describes an example of the application of such curious agents in the evaluation of the curating of an exhibition in an art gallery.

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Alexander, Perry. "Task Analysis and Design Plans in Formal Specification Design." International Journal of Software Engineering and Knowledge Engineering 08, no. 02 (June 1998): 223–52. http://dx.doi.org/10.1142/s0218194098000133.

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This paper presents BENTON, a prototype system demonstrating task analysis and multi-agent reasoning applied to formal specification synthesis. BENTON transforms specifications written as attribute-value pairs into Larch Modula-3 interface language and Larch Shared Language specifications. BENTON decomposes the software specification design task into synthesis, analysis and evaluation subtasks. Each subtask is assigned a specific design method based on problem and domain characteristics. This task analysis is achieved using blackboard knowledge sources and multi-agent reasoning employing design plans to implement different problem solving methods. Knowledge sources representing different problem solving methodologies monitor blackboard spaces and activate when they are applicable. When executed, Design plans send subtasks to agents that select from available problem solving methodologies. BENTON agents and knowledge sources use case-based reasoning, schemata-based reasoning and procedure execution as their fundamental reasoning methods. This paper presents an overview of the BENTON design model, its agent architecture and plan execution capabilities, and two annotated examples of BENTON problem solving activities.

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GERO, JOHN S., and FRANCES M. T. BRAZIER. "Special Issue: Intelligent agents in design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 2 (May 2004): 113. http://dx.doi.org/10.1017/s0890060404040089.

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This Special Issue had its genesis in an international Workshop on Agents in Design held in June 2002, at MIT by the Guest Editors. Computational agents have been developed within the artificial intelligence community over an extended period. The concept of an agent can be traced to Carl Hewitt's 1977 work on “actors.” Hewitt defined actors as self-contained, interactive, and concurrently executing objects. Since then, considerable research has gone into developing the concept of an agent and into formalizing agents, developing multiagent systems, and exploring their use. The use of agents in design is more recent, and the first PhDs in the area appeared in the early 1990s. Although a precise and unique definition of an agent has yet to be agreed upon, one distinguishing characteristic of an agent is that it exhibits autonomous behavior. Research on agents in design focuses on two primary areas: how to make agents useful in design, and how to apply them to design tasks. This Special Issue has papers from both areas.

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Zhang, Haolan, Wenhua Zeng, and der van. "A reusable agent design pattern with flexibility and extensibility." Computer Science and Information Systems 8, no. 4 (2011): 1229–50. http://dx.doi.org/10.2298/csis110304048z.

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Intelligent agent-based systems are regarded as the promising technology in bridging the gap between the physical world and cyber-applications. In spite of the rising demands for reusable information systems; current designs are still insufficient in providing efficient reusable mechanisms for system design. One of the major problems hinders the development of information reuse in most traditional systems is the lack of the autonomous character among system modules or subsystems. The emergence of agent technology is able to solve the problem plaguing many traditional systems. Existing agent design models create an agent as a sole system with built-in domain-specific capabilities. However, this design pattern causes several problems while matching and updating agents? capabilities due to the built-in design pattern in these models decreases agents? extensibility, flexibility and reusability. In this paper we introduce a novel design for agent-based systems, which is able to provide an efficient design pattern for improving the reusability, extensibility and flexibility of agent design. The novel agent capability design offers an open and flexible structure; and implements several practical algorithms that can improve the system performance. An experimental program based on several practical cases has been developed to evaluate the performance of the proposed design. The empirical results reveal the efficiency of the new agent design pattern.

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Wayllace, Christabel. "Stochastic Goal Recognition Design." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9904–5. http://dx.doi.org/10.1609/aaai.v33i01.33019904.

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Given an environment and a set of allowed modifications, the task of goal recognition design (GRD) is to select a valid set of modifications that minimizes the maximal number of steps an agent can take before its goal is revealed to an observer. This document presents an extension of GRD to the stochastic domain: the Stochastic Goal Recognition Design (S-GRD). The GRD framework aims to consider: (1) Stochastic agent action outcomes; (2) Partial observability of agent states and actions; and (3) Suboptimal agents. In this abstract we present the progress made towards the final objective as well as a timeline of projected conclusion.

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Oja, M., B. Tamm, and K. Taveter. "AGENT-BASED SOFTWARE DESIGN." Proceedings of the Estonian Academy of Sciences. Engineering 7, no. 1 (2001): 5. http://dx.doi.org/10.3176/eng.2001.1.02.

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CVETKOVIĆ, DRAGAN, and IAN PARMEE. "Agent-based support within an interactive evolutionary design system." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 5 (November 2002): 331–42. http://dx.doi.org/10.1017/s0890060402165012.

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This paper describes the use of software agents within an interactive evolutionary conceptual design system. Several different agent classes are introduced (search agents, interface agents, and information agents) and their function within the system is explained. A preference modification agent is developed and an example is given illustrating the use of agents in preference modeling.

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E. Ehimwenma, Kennedy, and Sujatha Krishnamoorthy. "Design and analysis of a multi-agent e-learning system using prometheus design tool." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 9. http://dx.doi.org/10.11591/ijai.v10.i1.pp9-23.

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Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents. The Pre-assessment System, as previously reported, is a multi-agent-based e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.

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Arora, Manish, and M. Syamala Devi. "Design of Multi Agent System for Resource Allocation and Monitoring." International Journal of Agent Technologies and Systems 3, no. 1 (January 2011): 1–10. http://dx.doi.org/10.4018/jats.2011010101.

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The objective of Resource Allocation and Monitoring System is to make the procedures involved in allocating fund resources to competing clients transparent so that deserving candidates get funds. Proactive and goal directed behaviour of agents make the system transparent and intelligent. This paper presents design of Multi Agent Systems for Resource Allocation and Monitoring using Agent Unified Modelling Language (AUML) and implementation in agent based development tool. At a conceptual level, three agents are identified with their roles and responsibilities. The identified agents, functionalities, and interactions are also included and results show that multi agent technology can be used for effective decision making for resource allocation and monitoring problem.

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Brazier, Frances M. T., Catholijn M. Jonker, Jan Treur, and Niek J. E. Wijngaards. "Compositional design of a generic design agent." Design Studies 22, no. 5 (September 2001): 439–71. http://dx.doi.org/10.1016/s0142-694x(00)00044-2.

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Hartline, Jason D. "Approximation in Mechanism Design." American Economic Review 102, no. 3 (May 1, 2012): 330–36. http://dx.doi.org/10.1257/aer.102.3.330.

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This paper considers three challenge areas for mechanism design and describes the role approximation plays in resolving them. Challenge 1: optimal mechanisms are finely tuned to precise details of the distribution on agent preferences. Challenge 2: in environments with multi-dimensional agent preferences economic analysis has failed to provide general characterizations optimal mechanisms. Challenge 3: optimal mechanisms are parameterized by unrealistic knowledge of the distribution of agents' private preferences. This paper surveys positive resolutions to these challenges with emphasis on basic techniques and their relevance to theory and practice.

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Feng, Zhi Jun, De Jian Zhou, Qi Fang Shen, and Yan Hui Chen. "Modeling of Network Cooperative Design of a Hydraulic System Based on Multi-Agent." Advanced Materials Research 317-319 (August 2011): 1424–28. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1424.

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To shorten the design period of a hydraulic system and achieve distributed cooperative design by experts, this paper proposes a model of the network collaborative design of a hydraulic system based on multi-agent technology. This model consists of three layers: the management agent layer, the middle agent layer and the resource agent layer. Its agent of the same layer adopts distributed structure, and each autonomous agent is constituted of a group of lower subordinate agents. This structure meets the need of distributed control of the cooperative design. By dynamic data exchange of agents, it carries out their communication, coordination and conflict resolution, and finally gets the global target. From the developed example, a multi-agent system can easily integrate existing engineering design tools with analysis tools. Its concurrent design process can improve design efficiency.

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Petrovica, Sintija, and Alla Anohina-Naumeca. "Design and implementation of agent interaction mechanisms for emotionally intelligent tutoring systems." Applied Computer Systems 13, no. 1 (November 8, 2012): 44–53. http://dx.doi.org/10.2478/v10312-012-0006-2.

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Abstract - Modern intelligent systems are built from intelligent agents, but usually agent interaction mechanisms are described at the general level omitting design and implementation details. The paper presents aspects of practical implementation of agent interaction mechanisms in JADE platform on the basis of the set of agents for modelling student’s emotions in intelligent tutoring systems. The concepts of an intelligent and affective tutoring system, agent, and agent interaction are explained, as well.

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Dworczak, Piotr. "Mechanism Design With Aftermarkets: Cutoff Mechanisms." Econometrica 88, no. 6 (2020): 2629–61. http://dx.doi.org/10.3982/ecta15768.

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I study a mechanism design problem in which a designer allocates a single good to one of several agents, and the mechanism is followed by an aftermarket—a post‐mechanism game played between the agent who acquired the good and third‐party market participants. The designer has preferences over final outcomes, but she cannot design the aftermarket. However, she can influence its information structure by publicly disclosing information elicited from the agents by the mechanism. I introduce a class of allocation and disclosure rules, called cutoff rules, that disclose information about the buyer's type only by revealing information about the realization of a random threshold (cutoff) that she had to outbid to win the object. When there is a single agent in the mechanism, I show that the optimal cutoff mechanism offers full privacy to the agent. In contrast, when there are multiple agents, the optimal cutoff mechanism may disclose information about the winner's type; I provide sufficient conditions for optimality of simple designs. I also characterize aftermarkets for which restricting attention to cutoff mechanisms is without loss of generality in a subclass of all feasible mechanisms satisfying additional conditions.

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WU, ZHICHAO, and ALEX H. B. DUFFY. "Modeling collective learning in design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 4 (November 2004): 289–313. http://dx.doi.org/10.1017/s0890060404040193.

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In this paper, a model of collective learning in design is developed in the context of team design. It explains that a team design activity uses input knowledge, environmental information, and design goals to produce output knowledge. A collective learning activity uses input knowledge from different agents and produces learned knowledge with the process of knowledge acquisition and transformation between different agents, which may be triggered by learning goals and rationale triggers. Different forms of collective learning were observed with respect to agent interactions, goal(s) of learning, and involvement of an agent. Three types of links between team design and collective learning were identified, namely teleological, rationale, and epistemic. Hypotheses of collective learning are made based upon existing theories and models in design and learning, which were tested using a protocol analysis approach. The model of collective learning in design is derived from the test results. The proposed model can be used as a basis to develop agent-based learning systems in design. In the future, collective learning between design teams, the links between collective learning and creativity, and computational support for collective learning can be investigated.

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Delaney, William J. "The Design Agent Process Strategy." IEEE Aerospace and Electronic Systems Magazine 2, no. 11 (November 1987): 20–24. http://dx.doi.org/10.1109/maes.1987.5005273.

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Bertola, P., and J. C. Teixeira. "Design as a knowledge agent." Design Studies 24, no. 2 (March 2003): 181–94. http://dx.doi.org/10.1016/s0142-694x(02)00036-4.

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Keddie, Daniel J., Graeme Moad, Ezio Rizzardo, and San H. Thang. "RAFT Agent Design and Synthesis." Macromolecules 45, no. 13 (May 21, 2012): 5321–42. http://dx.doi.org/10.1021/ma300410v.

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Yu, Bo, Zi Xian Zhang, Yi Xiong Feng, Luis Ariel Diago, and Ichiro Hagiwara. "Cooperation Design System Based on Mobile-C Agent Platform." Advanced Engineering Forum 1 (September 2011): 16–20. http://dx.doi.org/10.4028/www.scientific.net/aef.1.16.

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Over the past decades, Distributed Systems (DS) have been adopted for industrial applications to improve the system efficiency because distributed architecture has advantages in resource utilization, fault toleration .etc. Multi-Agent System (MAS) arises from combination of the theories of artificial intelligence and distributed systems. One character of MAS is their self-organization, so how to implement an effective mechanism for self-organization of agents is important to a MAS system, this paper describes the design and implementation of a Mobile-C based agent management system, in which Mobile-C was adopted as the implementation platform, and this paper also described an agent-based cooperative design application using this system to manage all the agents involved.

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Chen, Chiung Hui. "Behavioral Mechanism: Advancing a Design State with Multi-Agents Coordinating." Applied Mechanics and Materials 148-149 (December 2011): 789–94. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.789.

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For the past two decades, several design support tools have been developed for both research and commercial purposes. Most are stand-alone tools; few are comprehensive or collaborative design environments. Agent technology is an emerging field and agent-based application design is still a pioneering discipline. The agent-oriented design is a new generation method of programming design that mainly uses the script language as a basis for development and that is suitable to design the multi-agent application system. Some tools have adopted the notion of computational agency. In the urban design environment, suppose design objects such as pavements, lamps and plazas can look after themselves and have its own behavioral mechanism, where would a design object acquire the knowledge that allows it to interact intelligently? This paper investigates the question through the notion of objects as agents in design and the purpose of this research is to propose a coordinative mechanism of agents of spatial objects by establishing this design agent.

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Liu, Hong, and Mingxi Tang. "Evolutionary design in a multi-agent design environment." Applied Soft Computing 6, no. 2 (January 2006): 207–20. http://dx.doi.org/10.1016/j.asoc.2005.01.003.

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Itazuro, Syo, Takahiro Uchiya, Tetsuo Kinoshita, and Ichi Takumi. "Development Support of Learning Agent on Repository-based Agent Framework." International Journal of Software Science and Computational Intelligence 4, no. 3 (July 2012): 62–79. http://dx.doi.org/10.4018/jssci.2012070104.

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Agent-oriented computing is a technique for generating an agent that operates autonomously according to behavioral knowledge. Moreover, an agent can have a characteristic called “learning” skill. More efficient operation of an agent can be expected by support an agent designer who designs and develops an agent equipped with “learning” skill. The authors propose a design support mechanism of a learning agent on a repository-based agent framework called the DASH framework. The proposed mechanism enables an agent designer to design and implement the learning agent without great expertise, thereby reducing the designer’s burden. Herein, they explain the DASH framework, Q-Learning, and the proposed design support mechanism. Moreover, the authors demonstrate the effectiveness of the proposed method using some experiments.

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YUAN, SOE-TSYR, and ZENG-LUNG WU. "AN INFRASTRUCTURE FOR ENGINEERING COOPERATIVE AGENTS." International Journal of Software Engineering and Knowledge Engineering 10, no. 06 (December 2000): 681–711. http://dx.doi.org/10.1142/s0218194000000377.

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Currently, systems of cooperative agents (multi-agent systems), possessing the capabilities of autonomy, adaptation, and cooperation, are being used in an increasingly wide variety of application areas, and the conversation-based multi-agent system design is the major design for those multi-agent systems. Supposedly, conversation-based multi-agent systems should have been prevailing enough for tackling dynamic aspects of problems in a variety of domains. However, for industries, multi-agent systems are still found to be in the birth stage where they only show their new values in anticipation for further explorations and improvements in order to attract critical mass of users of information executives or software developers. Nevertheless, what are the success factors that can result in a critical mass of multi-agent system designers? This paper shows one possible success factor — an infrastructure for the bottom-up design of multi-agent systems. The bottom-up design makes it possible for agents to be reassembled into multi-agent systems and reused as needed. However, what do we need to successfully support the bottom-up design? This paper is the first attempt to present a tool that fully supports the bottom-up design of multi-agent systems. The tool has three parts. The first part is a wrapper that wraps each agent so that it exempts the designers from the careful detailed deployment of the inter-relationships between cooperation knowledge and task knowledge inside the agent. This wrapper should be independent of the functions of agents. The second part is an environment that can support the wrapper to automate the cooperation process on behalf of agents. The third part is a graphical assembly panel for developers to visually configure wrapped agents residing at different places of the Internet into a working multi-agent system.

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Chen, Shu-Heng. "Neuroeconomics and Agent-Based Computational Economics." International Journal of Applied Behavioral Economics 3, no. 2 (April 2014): 15–34. http://dx.doi.org/10.4018/ijabe.2014040102.

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Recently, the relation between neuroeconomics and agent-based computational economics (ACE) has become an issue concerning the agent-based economics community. Neuroeconomics can interest agent-based economists when they are inquiring for the foundation or the principle of the software-agent design. It has been shown in many studies that the design of software agents is non-trivial and can determine what will emerge from the bottom. Therefore, it has been quested for rather a period regarding whether anyone can sensibly design these software agents, including both the choice of software agent models, such as reinforcement learning, and the parameter setting associated with the chosen model, such as risk attitude. In this paper, the author will start a formal inquiry by focusing on examining the models and parameters used to build software agents.

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Tang, Jin Peng, Ling Lin Li, Shu Kun Liu, De Peng Hu, Me Ling Cai, and Xiao Qiu Liu. "Research of Intersection Navigation Algorithm Based on Intelligent Agent." Applied Mechanics and Materials 641-642 (September 2014): 690–93. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.690.

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In order to improve the vehicle alone path finding way, put forward a kind of road navigation algorithm based on agent. First put forward the intelligent design method, and then the intelligent design is mapped to different kinds of the intelligent agents. And then expounds the function of operation control agent, agent, Lane agent and intersection agent needs to realize. Finally, based on these agents of the navigation algorithm is designed using Dijkstra algorithm.

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Krening, Samantha, and Karen M. Feigh. "Characteristics that Influence Perceived Intelligence in AI Design." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1637–41. http://dx.doi.org/10.1177/1541931218621371.

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A goal of interactive machine learning (IML) is to create robots or intelligent agents that can be easily taught how to perform tasks by individuals with no specialized training. To achieve that goal, researchers and designers must understand how certain design decisions impact the human’s experience of teaching the agent, such as influencing the agent’s perceived intelligence. We posit that the type of feedback a robot can learn from affects the perceived intelligence of the robot, similar to its physical appearance. This study investigated two methods of natural language instruction: critique and action advice. We conducted a human-in-the-loop experiment in which people trained two agents with different teaching methods but, unknown to each participant, the same underlying machine learning algorithm. The results show an agent that learns from binary good/bad critique is perceived as less intelligent than an agent that can learn from action instructions, even if the underlying machine learning agent is the same. In addition to the complexity of the input, other design characteristics we found that influence the agent’s perceived intelligence are: compliance, responsiveness, effort, transparency, and robustness.

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SHEN, WEIMING, and JEAN-PAUL A. BARTHES. "AN EXPERIMENTAL MULTI-AGENT ENVIRONMENT FOR ENGINEERING DESIGN." International Journal of Cooperative Information Systems 05, no. 02n03 (June 1996): 131–51. http://dx.doi.org/10.1142/s0218843096000063.

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Real world engineering design projects require the cooperation of multidisciplinary design teams using sophisticated and powerful engineering tools. The individuals or the individual groups of the multidisciplinary design teams work in parallel and independently often for quite a long time with different tools located on various sites. In order to ensure the coordination of design activities in the different groups or the cooperation among the different tools, it is necessary to develop an efficient design environment. This paper discusses a distributed architecture for integrating such engineering tools in an open design environment, organized as a population of asynchronous cognitive agents. Before introducing the general architecture and the communication protocol, issues about an agent architecture and inter-agent communications are discussed. A prototype of such an environment with seven independent agents located in several workstations and microcomputers is then presented and demonstrated on an example of a small mechanical design.

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Bhargava, Hemant K., and Olivier Rubel. "Sales Force Compensation Design for Two-Sided Market Platforms." Journal of Marketing Research 56, no. 4 (June 3, 2019): 666–78. http://dx.doi.org/10.1177/0022243719825818.

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The authors study the use of sales agents for network mobilization in a two-sided market platform that connects buyers and sellers, and they examine how the presence of direct and indirect network effects influences the design of the sales compensation plan. They employ a principal–agent model in which the firm tasks sales agents to mobilize the side of the platform that it monetizes (i.e., sellers). Specifically, the presence of network effects alters the agency relationship between the firm and the sales agent, requiring the platform firm to alter the compensation design, and the nature of the alteration depends on whether the network effects are direct or indirect and positive or negative. The authors first show how the agent’s compensation plan should account for different types of network effects. They then establish that when the platform firm compensates the agent solely on the basis of network mobilization on the side cultivated by the agent (sellers), as intuition would suggest, it will not fully capitalize on the advantage of positive network effects; that is, profit can be lower under stronger network effects. To overcome this limitation, the platform should link the agent’s pay to a second metric, specifically, network mobilization on the buyer side, even though the agent is not assigned to that side. This design induces a positive relation between the strength of network effects and profit. This research underlines the complexity and richness of network effects and provides managers with new insights regarding the design of sales agents’ compensation plans for platforms.

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Tichý, Pavel, Petr Kadera, Raymond J. Staron, Pavel Vrba, and Vladimír Mařík. "Agent Development Environment for Multi-agent System Design and Integration." IFAC Proceedings Volumes 43, no. 4 (2010): 96–101. http://dx.doi.org/10.3182/20100701-2-pt-4011.00018.

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Tichý, Pavel, Petr Kadera, Raymond J. Staron, Pavel Vrba, and Vladimír Mařík. "Multi-agent system design and integration via agent development environment." Engineering Applications of Artificial Intelligence 25, no. 4 (June 2012): 846–52. http://dx.doi.org/10.1016/j.engappai.2011.09.021.

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BRAZIER, F. M. T., C. M. JONKER, J. TREUR, and N. J. E. WIJNGAARDS. "DELIBERATIVE EVOLUTION IN MULTI-AGENT SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 11, no. 05 (October 2001): 559–81. http://dx.doi.org/10.1142/s0218194001000670.

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Evolution of automated systems, in particular evolution of automated agents based on agent deliberation, is the topic of this paper. Evolution is not a merely material process, it requires interaction within and between individuals, their environments and societies of agents. An architecture for an individual agent capable of (1) deliberation about the creation of new agents, and (2) (run-time) creation of a new agent on the basis of this, is presented. The agent architecture is based on an existing generic agent model, and includes explicit formal conceptual representations of both design structures of agents and (behavioural) properties of agents. The process of deliberation is based on an existing generic reasoning model of design. The architecture has been designed using the compositional development method DESIRE, and has been tested in a prototype implementation.

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Grecu, Dan L., and David C. Brown. "Design agents that learn." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, no. 2 (April 1996): 149–50. http://dx.doi.org/10.1017/s0890060400001426.

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This research originates in the work started several years ago at Worcester Polytechnic Institute dedicated to the investigation, modelling and evaluation of multiagent based design. The main thrust behind our approach is the idea of finding elementary patterns of agent problem-solving and interaction in design tasks. To achieve this goal we introduced and defined the concept of Single Function Agents, (SiFAs) (Dunskus, 1995; SiFA, 1995). SiFAs are agents specialized to perform one single generic function during the design process. Some typical functions would be selection, evaluation, and critique. These types of agents can be instantiated for different, particular design domains.

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YAMAMOTO, Saori, and Yugo TAKEUCHI. "Human-Agent Interaction Design for Reducing Indebtedness." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 27, no. 6 (2015): 898–908. http://dx.doi.org/10.3156/jsoft.27.898.

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KARAGEORGOS, ANTHONY, NIKOLAY MEHANDJIEV, and SIMON THOMPSON. "RAMASD: a semi-automatic method for designing agent organisations." Knowledge Engineering Review 17, no. 4 (December 2002): 331–58. http://dx.doi.org/10.1017/s0269888903000572.

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Designing realistic multi-agent systems is a complex process, which involves specifying not only the functionality of individual agents, but also the authority relationships and lines of communication existing among them. In other words, designing a multi-agent system refers to designing an agent organisation. Existing methodologies follow a wide variety of approaches to designing agent organisations, but they do not provide adequate support for the decisions involved in moving from analysis to design. Instead, they require designers to make ad hoc design decisions while working at a low level of abstraction.We have developed RAMASD (Role Algebraic Multi-Agent System Design), a method for semi-automatic design of agent organisations based on the concept of role models as first-class design constructs. Role models represent agent behaviour, and the design of the agent system is done by systematically allocating roles to agents. The core of this method is a formal model of basic relations between roles, which we call role algebra. The semantics of this role-relationships model are formally defined using a two-sorted algebra.In this paper, we review existing agent system design methodologies to highlight areas where further work is required, describe how our method can address some of the outstanding issues and demonstrate its application to a case study involving telephone repair service teams.

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Fikri, Muhamad Rausyan, and Djati Wibowo Djamari. "Full-State Feedback Control Design for Shape Formation using Linear Quadratic Regulator." Indonesian Journal of Computing, Engineering and Design (IJoCED) 2, no. 2 (October 1, 2020): 83. http://dx.doi.org/10.35806/ijoced.v2i2.114.

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This study investigated the capability of a group of agents to form a desired shape formation by designing the feedback control using a linear quadratic regulator. In real application, the state condition of agents may change due to some particular problems such as a slow input response. In order to compensate for the problem that affects agent-to-agent coordination, a robust regulator was implemented into the formation algorithm. In this study, a linear quadratic regulator as the full-state feedback of robust regulator method for shape formation was considered. The result showed that a group of agents can form the desired shape (square) formation with a modification of the trajectory shape of each agent. The results were validated through numerical experiments.

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Keren, Sarah, Avigdor Gal, and Erez Karpas. "Goal Recognition Design in Deterministic Environments." Journal of Artificial Intelligence Research 65 (July 9, 2019): 209–69. http://dx.doi.org/10.1613/jair.1.11551.

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Goal recognition design (GRD) facilitates understanding the goals of acting agents through the analysis and redesign of goal recognition models, thus offering a solution for assessing and minimizing the maximal progress of any agent in the model before goal recognition is guaranteed. In a nutshell, given a model of a domain and a set of possible goals, a solution to a GRD problem determines (1) the extent to which actions performed by an agent within the model reveal the agent’s objective; and (2) how best to modify the model so that the objective of an agent can be detected as early as possible. This approach is relevant to any domain in which rapid goal recognition is essential and the model design can be controlled. Applications include intrusion detection, assisted cognition, computer games, and human-robot collaboration. A GRD problem has two components: the analyzed goal recognition setting, and a design model specifying the possible ways the environment in which agents act can be modified so as to facilitate recognition. This work formulates a general framework for GRD in deterministic and partially observable environments, and offers a toolbox of solutions for evaluating and optimizing model quality for various settings. For the purpose of evaluation we suggest the worst case distinctiveness (WCD) measure, which represents the maximal cost of a path an agent may follow before its goal can be inferred by a goal recognition system. We offer novel compilations to classical planning for calculating WCD in settings where agents are bounded-suboptimal. We then suggest methods for minimizing WCD by searching for an optimal redesign strategy within the space of possible modifications, and using pruning to increase efficiency. We support our approach with an empirical evaluation that measures WCD in a variety of GRD settings and tests the efficiency of our compilation-based methods for computing it. We also examine the effectiveness of reducing WCD via redesign and the performance gain brought about by our proposed pruning strategy.

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OLSON, JESSE T., and JONATHAN CAGAN. "Interagent ties in team-based computational configuration design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 2 (May 2004): 135–52. http://dx.doi.org/10.1017/s0890060404040107.

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Organizational research has shown that effectively structuring the resources (human, informational, computational) available to an organization can significantly improve its collective computational capacity. Central to this improved capacity is the manner in which the organization's member agents are related. This study is an initial investigation into the role and potential of interagent ties in computational teaming. A computational team-based model, designed to more fully integrate agent ties, is created and presented. It is applied to a bulk manufacturing process-planning problem and its performance compared against a previously tested agent-based algorithm without these agent relationships. The performance of the new agent method showed significant improvement over the previous method: improving solution quality 280% and increasing solution identification per unit time an entire order of magnitude. A statistical examination of the new algorithm confirms that agent interdependencies are the strongest and most consistent performance effects leading to the observed improvements. This study illustrates that the interagent ties associated with team collaboration can be a highly effective method of improving computational design performance, and the results are promising indications that the application of organization constructs within a computational context may significantly improve computational problem solving.

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Peled, Noam, Ya'akov Gal, and Sarit Kraus. "An Agent Design for Repeated Negotiation and Information Revelation with People." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 789–95. http://dx.doi.org/10.1609/aaai.v27i1.8619.

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Many negotiations in the real world are characterized by incomplete information, and participants' success depends on their ability to reveal information in a way that facilitates agreement without compromising the individual gains of agents. This paper presents a novel agent design for repeated negotiation in incomplete information settings that learns to reveal information strategically during the negotiation process. The agent used classical machine learning techniques to predict how people make and respond to offers during the negotiation, how they reveal information and their response to potential revelation actions by the agent. The agent was evaluated empirically in an extensive empirical study spanning hundreds of human subjects. Results show that the agent was able to outperform people. In particular, it learned (1) to make offers that were beneficial to people while not compromising its own benefit; (2) to incrementally reveal information to people in a way that increased its expected performance. The approach generalizes to new settings without the need to acquire additional data. This work demonstrates the efficacy of combining machine learning with opponent modeling techniques towards the design of computer agents for negotiating with people in settings of incomplete information.

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Park, Hisup, Mark R. Cutkosky, Andrew B. Conru, and Soo-Hong Lee. "An agent-based approach to concurrent cable harness design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8, no. 1 (1994): 45–61. http://dx.doi.org/10.1017/s0890060400000457.

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AbstractAn approach to providing computational support for concurrent design is discussed in the context of an industrial cable harness design problem. Key issues include the development of an architecture that supports collaboration among specialists, the development of hierarchical representations that capture different characteristics of the design, and the decomposition of tasks to achieve a trade-off between efficiency and robustness. An architecture is presented in which the main design tasks are supported by agents – asynchronous and semiautonomous modules that automate routine design tasks and provide specialized interfaces for working on particular aspects of the design. The agent communication and coordination mechanisms permit members of an engineering team to work concurrently, at different levels of detail and on different versions of the design. The design is represented hierarchically, with detailed models maintained by the participating agents. Abstractions of the detailed models, called “agent model images,” are shared with other agents. In conjunction with the architecture and design representations, issues pertaining to the exchange of information among different views of the design, management of dependencies and constraints, and propagation of design changes are discussed.

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CHANDER, P. G., T. RADHAKRISHNAN, and R. SHINGHAL. "Design issues in implementing a cooperative search among heterogeneous agents to aid information management." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 1 (January 2001): 51–65. http://dx.doi.org/10.1017/s0890060401151036.

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Searching for information is an ubiquitous need in today's data-oriented environments. However, a request for search often entails the service and cooperation of tools managing a diversified set of tasks. In this article, we explore how tools in the form of cooperating agents can be deployed for information management. We describe an agent framework called GAME (goal-oriented, agent-managed environment), and focus on how GAME agents search cooperatively for information requested by a user. Cooperative search entails several issues such as coordinating agent activities, maintaining transparency to agent heterogeneity, and designing information formats to be shared among the agents that require examination. This article analyzes these issues and describes how they are handled in the GAME framework. Cooperative search effectively supports collaboration and information sharing not only among agents in a domain, but also among GAME agents developed across domains. We illustrate the application of cooperative search in task-oriented domains such as Manufacturing and Front Office showing how GAME promotes intradomain and interdomain collaboration in a Factory environment.

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Fougères, Alain-Jérôme, and Egon Ostrosi. "Intelligent agents for feature modelling in computer aided design." Journal of Computational Design and Engineering 5, no. 1 (November 6, 2017): 19–40. http://dx.doi.org/10.1016/j.jcde.2017.11.001.

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Abstract CAD modelling can be referred to as the process of generating an integrated multiple view model as a representation of multiple views of engineering design. In many situations, a change in the model of one view may conflict with the models of other views. In such situations, the model of some views needs to be adapted in order to make all models consistent. Thus, CAD models should be capable of adapting themselves to new situations. Recently, agent based technologies have been considered in order to increase both knowledge level and intelligence of real and virtual objects. The contribution of this paper consists in introducing the intelligent agents in intelligent CAD modelling. The proposed agents are elementary geometrical and topological objects. They incorporate the functions of observation, decision and action, and possess their own knowledge. Agents have the capacity of communication and inference based on the feature grammars. They are modelled as bio-dynamic objects that enjoy the properties of fusion, division and multiplication. Being aware of the context, the proposed agents interact to form potential regional transitory communities, called regions. Being aware of their belonging in a region, agents interact by generating virtual links (virtual extensions). These virtual links produce: (a) fusion of agents, (b) division of agents and c) multiplication of agents. The emerged agents interact with the other agents in a region to recognize each other and to form specific sub-communities, called intelligent features. From a CAD software development point of view, this paper advocates the idea of a new phase of CAD system development based on the agent-oriented programming (AOP) paradigm. Highlights This paper proposes the agent paradigm for intelligent CAD modelling. Second section presents the state of art. In the third section, using the linguistic hypothesis of product design, a feature modelling formalism is presented. Fourth section presents a formal model for agent modelling. In the fifth section, agents for feature generation and modelling are formalised and modelled. The sixth section presents the application of the method. Finally, in the last section, the conclusion and future developments are proposed.

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Wang, Chen Shu. "Hybrid Intelligence Agents Architecture Design for Product Return Administration." Advanced Materials Research 403-408 (November 2011): 3339–43. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3339.

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Return is a critical but controversial issue. To deal with such a vague return problem, business must improve information transparency about end users’ return activities. This research proposed an agent-based architecture for return administration. The intelligent return administration expert system (iRAES) architecture consists of two KDD mechanisms and two intelligent agents that can predict the possibility of the end user will return the product (via return diagnosis agent, RDA) and provide return centre staff with recommendations for return administration (via return recommender agent, RRA). iRAES is implemented successfully and adopts hybrid artificial intelligent algorithms, including the following: data mining is employed to implement the RDA agent, and case-based reasoning is adopted to design the RRA agent. A demonstrated 3C-iShop scenario is presented to illustrate the feasibility and efficiency of iRAES architecture. As the experiment results show, iRAES can decrease the 70% effort for return administration evaluation and improve performance with return administration suggestions by 37%. Therefore, return administration and the knowledge management about product return can be accelerated via iRAES.

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DeVries, Levi, Aaron Sims, and Michael D. M. Kutzer. "Kernel Design and Distributed, Self-Triggered Control for Coordination of Autonomous Multi-Agent Configurations." Robotica 36, no. 7 (March 27, 2018): 1077–97. http://dx.doi.org/10.1017/s0263574718000231.

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SUMMARYAutonomous multi-agent systems show promise in countless applications, but can be hindered in environments where inter-agent communication is limited. In such cases, this paper considers a scenario where agents communicate intermittently through a cloud server. We derive a graph transformation mapping the kernel of a graph's Laplacian to a desired configuration vector while retaining graph topology characteristics. The transformation facilitates derivation of a self-triggered controller driving agents to prescribed configurations while regulating instances of inter-agent communication. Experimental validation of the theoretical results shows the self-triggered approach drives agents to a desired configuration using fewer control updates than traditional periodic implementations.

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Lhaksmana, Kemas M., Yohei Murakami, and Toru Ishida. "Role-Based Modeling for Designing Agent Behavior in Self-Organizing Multi-Agent Systems." International Journal of Software Engineering and Knowledge Engineering 28, no. 01 (January 2018): 79–96. http://dx.doi.org/10.1142/s0218194018500043.

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Self-organization has been proposed to be implemented in complex systems which require the automation capabilities to govern itself and to adapt upon changes. Self-organizing systems can be modeled as multi-agent systems (MAS) since they share common characteristics in that they consist of multiple autonomous systems. However, most existing MAS engineering methodologies do not fully support self-organizing systems design since they require predefined goals and agent behaviors, which is not the case in self-organizing systems. Another feature that is currently not supported for designing self-organizing MAS is the separation between the design of agent behaviors and behavior adaptation, i.e. how agents adapt their behaviors to respond upon changes. To tackle these issues, this paper proposes a role modeling method, in which agent behaviors are represented as roles, to design how agents perform behavior adaptation at runtime by switching between roles. The applicability of the proposed role modeling method is evaluated in a case study of a self-organizing smart transportation system.

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Hou, Yue Min, and Li Hong Ji. "Configuring Agents for the Design of Chamber Systems of IC Equipments." Applied Mechanics and Materials 490-491 (January 2014): 498–503. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.498.

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The availability of multi-fields simulation and rapid design is at core of a competitive design ability of chamber systems for IC (integrated Circuit) manufacturing. A computer system is required to provide rapid response for both new designs and processing validations, so programming the design and simulation procedure is necessary. However, some steps of the design and simulation procedure are not clear in the initial design stage in many cases. They only can be decided when part of the structure is visible, particularly when novel structures or new working principles are used. The use of agents is one way to collect information that is required but cannot be pre-programmed in computers. This paper presents a generic method to configure agents, including an analysis framework using a six stage design process, the agent analysis for general design and simulation process, the procedures of design and processing simulation of chambers, the configuration of agents, and finally the conclusion and further works. This paper contributes a generic agent design method.

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Zhang, Li, Zhi Qi, Qian Zhu Wang, Xing Ping Wang, and Xin Shen. "Building a Multi-Agent System for Emergency Logistics Collaborative Decision." Applied Mechanics and Materials 513-517 (February 2014): 2041–44. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2041.

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Currently, the decision making of emergency logistics is faced with increasing challenges caused by deficient information, uncertain requirement and shortest response time. Agent-based modeling and multi-agent system have been proved as a promising ways in this field. Based on the previous work of emergency logistics decision framework, this paper presents a detailed design of agent internal structure of the emergency logistics multi-agent system. Some typical agents, such as logistics entity agent, task distribution agent and ontology visiting agent, are discussed from the composed function modules to the specific implementation. As the illustrative examples, the design of these primary agents can characterizes the basic structure of another agent in the emergency logistics multi-agent system, and it will be considered as the effective reference for system implementation.

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Pudāne, Māra, and Egons Lavendelis. "General Guidelines for Design of Affective Multi-Agent Systems." Applied Computer Systems 22, no. 1 (December 1, 2017): 5–12. http://dx.doi.org/10.1515/acss-2017-0012.

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Abstract The paper presents general guidelines for designing affective multi-agent systems (affective MASs). The guidelines aim at extending the existing agent-oriented software engineering (AOSE) methodologies to enable them to design affective MASs. The reason why affective mechanisms need specific attention during the design is the fact that the way how both rational tasks and interactions are done differ based on the affective state of the agents. Thus, the paper extends the traditional design approaches with the design of affective mechanisms and includes them in the design of the system as a whole.

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Liu, Yongjia, Yu Jiang, and Ning Ge. "Design of Personal Terminal DNS Agent." Journal of Communications and Information Networks 6, no. 3 (September 2021): 251–66. http://dx.doi.org/10.23919/jcin.2021.9549121.

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Journal articles on the topic 'Design Agent'

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