Journal articles on the topic 'Autonomous agents and multiagent systems'
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Ur Rehman, Shafiq, and Aamer Nadeem. "An Approach to Model Based Testing of Multiagent Systems." Scientific World Journal 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/925206.
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Autonomous agents perform on behalf of the user to achieve defined goals or objectives. They are situated in dynamic environment and are able to operate autonomously to achieve their goals. In a multiagent system, agents cooperate with each other to achieve a common goal. Testing of multiagent systems is a challenging task due to the autonomous and proactive behavior of agents. However, testing is required to build confidence into the working of a multiagent system. Prometheus methodology is a commonly used approach to design multiagents systems. Systematic and thorough testing of each interaction is necessary. This paper proposes a novel approach to testing of multiagent systems based on Prometheus design artifacts. In the proposed approach, different interactions between the agent and actors are considered to test the multiagent system. These interactions include percepts and actions along with messages between the agents which can be modeled in a protocol diagram. The protocol diagram is converted into a protocol graph, on which different coverage criteria are applied to generate test paths that cover interactions between the agents. A prototype tool has been developed to generate test paths from protocol graph according to the specified coverage criterion.
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Endriss, Ulle, Ann Nowé, Maria Gini, Victor Lesser, Michael Luck, Ana Paiva, and Jaime Sichman. "Autonomous agents and multiagent systems." AI Matters 7, no. 3 (September 2021): 29–37. http://dx.doi.org/10.1145/3511322.3511329.
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The 2021 edition of AAMAS, the International Conference on Autonomous Agents and Multiagent Systems, took place from the 3rd to 7th of May 2021 (aamas2021.soton.ac.uk). This year it was organized in the form of a virtual event and attracted over 1,000 registered participants. As every year, the conference featured an exciting programme of contributed talks, keynotes addresses, tutorials, affiliated workshops, a doctoral consortium, and more.
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Dahlstedt, Palle, and Peter McBurney. "Musical Agents: Toward Computer-Aided Music Composition Using Autonomous Software Agents." Leonardo 39, no. 5 (October 2006): 469–70. http://dx.doi.org/10.1162/leon.2006.39.5.469.
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The authors, a composer and a computer scientist, discuss their collaborative research on the use of multiagent systems and their applicability to music and musical composition. They describe the development of software and techniques for the composition of generative music.
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Muñoz, Antonio, Pablo Anton, and Antonio Maña. "Multiagent Systems Protection." Advances in Software Engineering 2011 (August 15, 2011): 1–9. http://dx.doi.org/10.1155/2011/281517.
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Agent-systems can bring important benefits especially in applications scenarios where highly distributed, autonomous, intelligence, self-organizing, and robust systems are required. Furthermore, the high levels of autonomy and self-organizations of agent systems provide excellent support for developments of systems in which dependability is essential. Both Ubiquitous Computing and Ambient Intelligence scenarios belong in this category. Unfortunately, the lack of appropriate security mechanisms, both their enforcement and usability, is hindering the application of this paradigm in real-world applications. Security issues play an important role in the development of multiagent systems and are considered to be one of the main issues to solve before agent technology is ready to be widely used outside the research community. In this paper, we present a software based solution for the protection of multiagent systems concentrating on the cooperative agents model and the protected computing approach.
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Hyso, Alketa, and Eva Cipi. "Autonomous Agents as Tools for Modeling and Building Complex Control Systems that Operate in Dynamic and Unpredictable Environment." International Journal of Business & Technology 1, no. 2 (May 2013): 47–53. http://dx.doi.org/10.33107/ijbte.2013.1.2.05.
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Complex control systems that operate in not entirely predictable environment have to deal with this environment in an autonomous manner using adaptability, the ability to predict environmental changes, and to maintain their integrity. Elements of the system must be able to find a new solution in a dynamic way. In this paper, we present the modeling of a traffic lights’ control system using a multivalent system. This is a large-scale distributed system, consisting of autonomous and rational traffic light agents, in which there is no centre imposing an outcome. Multiagent system brings another kind of organization of the distributed control. The information is distributed over the agents. The behavior of the other agents is incorporated into the making decision process of the agent. We apply different control algorithms in our multiagent simulation environment and show that using multiagent systems in dynamic and unpredictable environment the control will be adoptable.
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Mackin, Kenneth James. "Autonomous Learning of Agent Communication and Group Behavior in Intelligent Multiagent Systems." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 15, no. 2 (2003): 187. http://dx.doi.org/10.3156/jsoft.15.187_2.
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Silva, Felipe Leno Da, and Anna Helena Reali Costa. "A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems." Journal of Artificial Intelligence Research 64 (March 11, 2019): 645–703. http://dx.doi.org/10.1613/jair.1.11396.
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Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions.
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Sonenberg, Liz, Peter Stone, Kagan Tumer, and Pinar Yolum. "Ten Years of AAMAS: Introduction to the Special Issue." AI Magazine 33, no. 3 (September 20, 2012): 11. http://dx.doi.org/10.1609/aimag.v33i3.2423.
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Panella, Alessandro. "Multiagent Stochastic Planning With Bayesian Policy Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 29, 2013): 1672–73. http://dx.doi.org/10.1609/aaai.v27i1.8506.
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When operating in stochastic, partially observable, multiagent settings, it is crucial to accurately predict the actions of other agents. In my thesis work, I propose methodologies for learning the policy of external agents from their observed behavior, in the form of finite state controllers. To perform this task, I adopt Bayesian learning algorithms based on nonparametric prior distributions, that provide the flexibility required to infer models of unknown complexity. These methods are to be embedded in decision making frameworks for autonomous planning in partially observable multiagent systems.
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Satybaldiyeva, A., A. Ismailova, R. Moldasheva, A. Mukhanova, and K. Kadirkulov. "ABSTRACT DATA TYPES FOR KNOWLEDGE REPRESENTATION AND SPECIFICATION OF MULTI-AGENT SYSTEMS." PHYSICO-MATHEMATICAL SERIES 2, no. 336 (April 15, 2021): 48–55. http://dx.doi.org/10.32014/2021.2518-1726.20.
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Distributed system is a group of decentralized interacting executers. Distributed algorithm is the communication protocol for a distributed system that transforms the group into a team to solve some task. Multiagent system is a distributed system that consists of autonomous reactive agents, i.e. executers which internal states can be characterized in terms Believes (B), Desires (D), and Intentions (I). Multiagent algorithm is a distributed algorithm for a multiagent system. The article discusses the basic concepts of agents and multi-agent systems. Also, two problems of multi-agent algorithms for representing knowledge in the context of Social Software Engineering are considered. A number of new multi-agent algorithms are presented, and their correctness is proved. The main characteristics of agents are provided, such as autonomy, proactivity, social ability, and reactivity; also, agents can have such additional characteristics as persistence, reasonability, performance, mobility, personality, and rationality. A number of new multi-agent algorithms are presented, and their correctness is proved. Two statements have been proved for solving RAM and MRP problems. This time we address a social issue of agent anonymity and privacy in these algo-rithms.
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Wang, Sung-Jung, and S. K. Jason Chang. "Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning." Journal of Advanced Transportation 2021 (July 2, 2021): 1–14. http://dx.doi.org/10.1155/2021/6654254.
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Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers.
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BOELLA, GUIDO, and LEENDERT VAN DER TORRE. "NORM NEGOTIATION IN MULTIAGENT SYSTEMS." International Journal of Cooperative Information Systems 16, no. 01 (March 2007): 97–122. http://dx.doi.org/10.1142/s0218843007001585.
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Normative multiagent systems provide agents with abilities to autonomously devise societies and organizations coordinating their behavior via social norms and laws. In this paper, we study how agents negotiate new social norms and when they accept them. We introduce a negotiation model based on what we call the social delegation cycle, which explains the negotiation of new social norms from agent desires in three steps. First, individual agents or their representatives negotiate social goals, then a social goal is negotiated in a social norm, and finally the social norm is accepted by the agents when it leads to fulfillment of the desires the cycle started with. We characterize the allowed proposals during social goal negotiation as mergers of the individual agent desires, and we characterize the allowed proposals during norm negotiation as both joint plans to achieve the social goal (obligations associated with the norm) and the associated sanctions or rewards (a control system associated with the norm). The norm is accepted when the norm is stable in the sense that agents will act according to the norm, and effective in the sense that fulfillment of the norm leads to achievement of the agents' desires. We also compare norm negotiation with contract negotiation and negotiation of the distribution of obligations.
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Gath, Max, Stefan Edelkamp, and Otthein Herzog. "Agent-Based Dispatching Enables Autonomous Groupage Traffic." Journal of Artificial Intelligence and Soft Computing Research 3, no. 1 (January 1, 2013): 27–40. http://dx.doi.org/10.2478/jaiscr-2014-0003.
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Abstract The complexity and dynamics in groupage traffic require flexible, efficient, and adaptive planning and control processes. The general problem of allocating orders to vehicles can be mapped into the Vehicle Routing Problem (VRP). However, in practical applications additional requirements complicate the dispatching processes and require a proactive and reactive system behavior. To enable automated dispatching processes, this article presents a multiagent system where the decision making is shifted to autonomous, interacting, intelligent agents. Beside the communication protocols and the agent architecture, the focus is on the individual decision making of the agents which meets the specific requirements in groupage traffic. To evaluate the approach we apply multiagent-based simulation and model several scenarios of real world infrastructures with orders provided by our industrial partner. Moreover, a case study is conducted which covers the autonomous groupage traffic in the current processes of our industrial parter. The results reveal that agent-based dispatching meets the sophisticated requirements of groupage traffic. Furthermore, the decision making supports the combination of pickup and delivery tours efficiently while satisfying logistic request priorities, time windows, and capacity constraints.
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Sinha, A., and D. Ghose. "Control of Multiagent Systems Using Linear Cyclic Pursuit With Heterogenous Controller Gains." Journal of Dynamic Systems, Measurement, and Control 129, no. 5 (November 30, 2006): 742–48. http://dx.doi.org/10.1115/1.2764505.
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In this paper, behavior of a group of autonomous mobile agents under cyclic pursuit is studied. Cyclic pursuit is a simple distributed control law, in which the agent i pursues agent i+1modn. The equations of motion are linear, with no kinematic constraints on motion. Behaviorally, they are identical but may have different controller gains. We generalize existing results in the literature, which consider only homogenous gains, to the case where controller gains are heterogenous. We show that, by selecting suitable controller gains, collective behavior of agents can be controlled significantly to obtain not only point convergence but also directed motion. In particular, we obtain analytical results that relate the controller gains to the direction of movement of the agents when the system is unstable. Invariance results with respect to the pursuit sequence are also proved. Finally, we also obtain some results that show some aspects of system behavior that is invariant with respect to finite switching of connections. Simulation experiments are given in support of the analytical results.
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Dresner, K., and P. Stone. "A Multiagent Approach to Autonomous Intersection Management." Journal of Artificial Intelligence Research 31 (March 31, 2008): 591–656. http://dx.doi.org/10.1613/jair.2502.
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Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field that focuses on integrating information technology with vehicles and transportation infrastructure to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated. The bottleneck for roadway efficiency will no longer be the drivers, but rather the mechanism by which those drivers' actions are coordinated. While open-road driving is a well-studied and more-or-less-solved problem, urban traffic scenarios, especially intersections, are much more challenging. We believe current methods for controlling traffic, specifically at intersections, will not be able to take advantage of the increased sensitivity and precision of autonomous vehicles as compared to human drivers. In this article, we suggest an alternative mechanism for coordinating the movement of autonomous vehicles through intersections. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this multiagent system, intersections use a new reservation-based approach built around a detailed communication protocol, which we also present. We demonstrate in simulation that our new mechanism has the potential to significantly outperform current intersection control technology -- traffic lights and stop signs. Because our mechanism can emulate a traffic light or stop sign, it subsumes the most popular current methods of intersection control. This article also presents two extensions to the mechanism. The first extension allows the system to control human-driven vehicles in addition to autonomous vehicles. The second gives priority to emergency vehicles without significant cost to civilian vehicles. The mechanism, including both extensions, is implemented and tested in simulation, and we present experimental results that strongly attest to the efficacy of this approach.
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Doshi, Prashant J. "Decision Making in Complex Multiagent Contexts: A Tale of Two Frameworks." AI Magazine 33, no. 4 (December 21, 2012): 82. http://dx.doi.org/10.1609/aimag.v33i4.2402.
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Decision making is a key feature of autonomous systems. It involves choosing optimally between different lines of action in various information contexts that range from perfectly knowing all aspects of the decision problem to having just partial knowledge about it. The physical context often includes other interacting autonomous systems, typically called agents. In this article, I focus on decision making in a multiagent context with partial information about the problem. Relevant research in this complex but realistic setting has converged around two complementary, general frameworks and also introduced myriad specializations on its way. I put the two frameworks, decentralized partially observable Markov decision process (Dec-POMDP) and the interactive partially observable Markov decision process (I-POMDP), in context and review the foundational algorithms for these frameworks, while briefly discussing the advances in their specializations. I conclude by examining the avenues that research pertaining to these frameworks is pursuing.
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Iyer, Karthik, and Michael N. Huhns. "Negotiation criteria for multiagent resource allocation." Knowledge Engineering Review 24, no. 2 (June 2009): 111–35. http://dx.doi.org/10.1017/s0269888909000204.
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AbstractNegotiation in a multiagent system is a topic of active interest for enabling the allocation of scarce resources among autonomous agents. This paper presents a discussion of the research on negotiation criteria, which puts in context the contributions to resource allocation from the fields of economics, mathematics, and multiagent systems. We group the criteria based on how they relate to each other as well as their historical origin. In addition, we present three new criteria: verifiability, dimensionality, and topology. The criteria are organized into five categories. The allocation category contains criteria concerning fairness and envy-freeness with respect to how resources are allocated to agents. The protocol category covers criteria for stability, strategy-proofness, and communication costs. The procedure category includes criteria about the complexity of allocation procedures. The resource category has criteria for the properties that various resources can take and how they affect allocation. The paper concludes with a discussion of the criteria for agent utility functions. The overall objectives of this paper are (1) to create a starting point for protocol engineering by future negotiation designers and (2) to enumerate the criteria and their measures that enable negotiation and allocation mechanisms to be compared objectively.
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Binmad, Ruchdee, and Mingchu Li. "Psychology-Inspired Trust Restoration Framework in Distributed Multiagent Systems." Scientific Programming 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/7515860.
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Trust violation during cooperation of autonomous agents in multiagent systems is usually unavoidable and can arise due to a wide number of reasons. From a psychological point of view, the violation of an agent’s trust is a result of one agent (which is a transgressor) expressing a very low weight on the welfare of another agent (which is a victim) by inflicting a high cost for a very small benefit. In order for the victim to make an effective decision about whether to cooperate or punish for the next interaction, a psychological variable called welfare tradeoff ratio (WTR) can be used to upregulate the transgressor’s disposition so that the number of exploitive behaviors that are likely to happen in the future will be decreased. In this paper, we propose computational models of metrics based on the welfare tradeoff ratio along with the way by which multiple metrics can be integrated to provide the final result. Additionally, a number of experiments based on social network analysis are conducted to evaluate the performance of the proposed framework and the results show that by implementing WTR the simulated network is able to deal with different levels of trust violation effectively.
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Bielecki, Andrzej, and Sylwia Nieszporska. "Analysis of Healthcare Systems by Using Systemic Approach." Complexity 2019 (April 21, 2019): 1–12. http://dx.doi.org/10.1155/2019/6807140.
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National healthcare systems in all countries do not act effectively. Therefore, especially strategies for introducing organizational innovation to public organization should be considered. The problem is how to organize the research in this field. One of the generally accepted solutions is the systemic approach to healthcare systems. In this paper multiagent systems theory and autonomous systems theory are applied to the analysis of main types of healthcare systems. Such analysis allows us to consider the system properties: the level of the autonomy, energy dissipation in the system, the payoff specificity (in the meaning of game theory), functional role of the agents in the system, the level of the agents’ cooperation, and delays in flows of money, requests, rules, and controls. As a result, some new functionalities of the healthcare system on the national level have been found and analysed. The aforementioned parameters are good tools to analyse the system functionality.
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Dror, Moshe, Bruce Hartman, Gary Knotts, and Daniel Zeng. "Randomized distributed access to mutually exclusive resources." Journal of Applied Mathematics and Decision Sciences 2005, no. 1 (January 1, 2005): 1–18. http://dx.doi.org/10.1155/jamds.2005.1.
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Many systems consist of a set of agents which must acquire exclusive access to resources from a shared pool. Coordination of agents in such systems is often implemented in the form of a centralized mechanism. The intervention of this type of mechanism, however, typically introduces significant computational overhead and reduces the amount of concurrent activity. Alternatives to centralized mechanisms exist, but they generally suffer from the need for extensive interagent communication. In this paper, we develop a randomized approach to make multiagent resource-allocation decisions with the objective of maximizing expected concurrency measured by the number of the active agents. This approach does not assume a centralized mechanism and has no need for interagent communication. Compared to existing autonomous-decentralized-decision-making (ADDM)-based approaches for resource-allocation, our work emphasizes achieving the highest degree of agent autonomy and is able to handle more general resource requirements.
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Johnson, W. Lewis, and James C. Lester. "Pedagogical Agents: Back to the Future." AI Magazine 39, no. 2 (July 1, 2018): 33–44. http://dx.doi.org/10.1609/aimag.v39i2.2793.
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Back in the 1990s we started work on pedagogical agents, a new user interface paradigm for interactive learning environments. Pedagogical agents are autonomous characters that inhabit learning environments and can engage with learners in rich, face-to-face interactions. Building on this work, in 2000 we, together with our colleague, Jeff Rickel, published an article on pedagogical agents that surveyed this new paradigm and discussed its potential. We made the case that pedagogical agents that interact with learners in natural, life-like ways can help learning environments achieve improved learning outcomes. This article has been widely cited, and was a winner of the 2017 IFAAMAS Award for Influential Papers in Autonomous Agents and Multiagent Systems (IFAAMAS, 2017). On the occasion of receiving the IFAAMAS award, and after twenty years of work on pedagogical agents, we decided to take another look at the future of the field. We’ll start by revisiting our predictions for pedagogical agents back in 2000, and examine which of those predictions panned out. Then, informed what we have learned since then, we will take another look at emerging trends and the future of pedagogical agents. Advances in natural language dialogue, affective computing, machine learning, virtual environments, and robotics are making possible even more lifelike and effective pedagogical agents, with potentially profound effects on the way people learn.
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Raju, Leo, R. S. Milton, and Senthilkumaran Mahadevan. "Multiagent Systems Based Modeling and Implementation of Dynamic Energy Management of Smart Microgrid Using MACSimJX." Scientific World Journal 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/9858101.
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The objective of this paper is implementation of multiagent system (MAS) for the advanced distributed energy management and demand side management of a solar microgrid. Initially, Java agent development environment (JADE) frame work is used to implement MAS based dynamic energy management of solar microgrid. Due to unstable nature of MATLAB, when dealing with multithreading environment, MAS operating in JADE is linked with the MATLAB using a middle ware called Multiagent Control Using Simulink with Jade Extension (MACSimJX). MACSimJX allows the solar microgrid components designed with MATLAB to be controlled by the corresponding agents of MAS. The microgrid environment variables are captured through sensors and given to agents through MATLAB/Simulink and after the agent operations in JADE, the results are given to the actuators through MATLAB for the implementation of dynamic operation in solar microgrid. MAS operating in JADE maximizes operational efficiency of solar microgrid by decentralized approach and increase in runtime efficiency due to JADE. Autonomous demand side management is implemented for optimizing the power exchange between main grid and microgrid with intermittent nature of solar power, randomness of load, and variation of noncritical load and grid price. These dynamics are considered for every time step and complex environment simulation is designed to emulate the distributed microgrid operations and evaluate the impact of agent operations.
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Shabani, Faridoon, Bijan Ranjbar, and Ali Ghadamyari. "An Adaptive -Based Formation Control for Multirobot Systems." ISRN Robotics 2013 (November 27, 2013): 1–12. http://dx.doi.org/10.5402/2013/192487.
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We describe a decentralized formation problem for multiple robots, where an formation controller is proposed. The network of dynamic agents with external disturbances and uncertainties are discussed in formation problems. We first describe how to design social potential fields to obtain a formation with the shape of a polygon. Then, we provide a formal proof of the asymptotic stability of the system, based on the definition of a proper Lyapunov function and technique. The advantages of the proposed controller can be listed as robustness to input nonlinearity, external disturbances, and model uncertainties, while applicability on a group of any autonomous systems with -degrees of freedom. Finally, simulation results are demonstrated for a multiagent formation problem of a group of six robots, illustrating the effective attenuation of approximation error and external disturbances, even in the case of agent failure or leader tracking.
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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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Weng, Yu, Haozhen Chu, and Zhaoyi Shi. "An Intelligent Offloading System Based on Multiagent Reinforcement Learning." Security and Communication Networks 2021 (March 24, 2021): 1–13. http://dx.doi.org/10.1155/2021/8830879.
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Intelligent vehicles have provided a variety of services; there is still a great challenge to execute some computing-intensive applications. Edge computing can provide plenty of computing resources for intelligent vehicles, because it offloads complex services from the base station (BS) to the edge computing nodes. Before the selection of the computing node for services, it is necessary to clarify the resource requirement of vehicles, the user mobility, and the situation of the mobile core network; they will affect the users’ quality of experience (QoE). To maximize the QoE, we use multiagent reinforcement learning to build an intelligent offloading system; we divide this goal into two suboptimization problems; they include global node scheduling and independent exploration of agents. We apply the improved Kuhn–Munkres (KM) algorithm to node scheduling and make full use of existing edge computing nodes; meanwhile, we guide intelligent vehicles to the potential areas of idle computing nodes; it can encourage their autonomous exploration. Finally, we make some performance evaluations to illustrate the effectiveness of our constructed system on the simulated dataset.
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Gascueña, José M., and Antonio Fernández-Caballero. "On the use of agent technology in intelligent, multisensory and distributed surveillance." Knowledge Engineering Review 26, no. 2 (May 12, 2011): 191–208. http://dx.doi.org/10.1017/s0269888911000026.
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AbstractThis article revises the state of the art of the application of agent technology within the scope of surveillance systems. Thus, the potential of the practical use of the concepts and technologies of the agent paradigm can be identified and evaluated in this domain. Current surveillance systems are noted for using several devices, heterogeneous in many instances, distributed along the observed scenario, while incorporating a certain degree of intelligence to alert the operator proactively to what is going on in the observed scenario and prevent the operator from having to observe the monitors continuously. The basic characteristics of the agents (autonomy, reactivity, proactiveness and social ability), along with multiagent systems’ characteristics (distributed data management, low coupling, robustness, communication and coordination between autonomous entities), suggest that the agency is a good choice for solving problems which appear and are dealt with in surveillance systems.
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Si, Huaiwei, Guozhen Tan, and Hao Zuo. "A Deep Coordination Graph Convolution Reinforcement Learning for Multi-Intelligent Vehicle Driving Policy." Wireless Communications and Mobile Computing 2022 (June 28, 2022): 1–13. http://dx.doi.org/10.1155/2022/9665421.
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With the growing up of Internet of Things technology, the application of Internet of Things has been popularized in the field of intelligent vehicles. Therefore, more artificial intelligence algorithms, especially DRL methods, are more widely used in autonomous driving. A large number of deep reinforcement learning (RL) technologies are continuously applied to the behavior planning module of single-vehicle autonomous driving in early. However, autonomous driving is an environment where multi-intelligent vehicles coexist, interact with each other, and dynamically change. In this environment, multiagent RL technology is one of the most promising technologies for solving the coordination behavior planning problem of multivehicles. However, the research related to this topic is rare. This paper introduces a dynamic coordination graph (CG) convolution technology for the cooperative learning of multi-intelligent vehicles. This method dynamically constructs a CG model among multiple vehicles, effectively reducing the impact of unrelated intelligent vehicles and simplifying the learning process. The relationship between intelligent vehicles is refined using the attention mechanism, and the graph convolution RL technology is used to simulate the message-passing aggregation algorithm to maximize the local utility and obtain the maximum joint utility to guide coordination learning. Driving samples are used as training data, and the model guided by reward shaping is combined with the model of the free graph convolution RL method, which enables our proposed method to achieve high gradualness and improve its learning efficiency. In addition, as the graph convolutional RL algorithm shares parameters between agents, it can easily build scales that are suitable for large-scale multiagent systems, such as traffic environments. Finally, the proposed algorithm is tested and verified for the multivehicle cooperative lane-changing problem in the simulation environment of autonomous driving. Experimental results show that our proposed method has better value function representation in that it can learn better coordination driving policies than traditional dynamic coordination algorithms.
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Kardas, Geylani. "Model-driven development of multiagent systems: a survey and evaluation." Knowledge Engineering Review 28, no. 4 (April 19, 2013): 479–503. http://dx.doi.org/10.1017/s0269888913000088.
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AbstractTo work in a higher abstraction level is of critical importance for the development of multiagent systems (MAS) since it is almost impossible to observe code-level details of such systems due to their internal complexity, distributedness and openness. As one of the promising software development approaches, model-driven development (MDD) aims to change the focus of software development from code to models. This paradigm shift, introduced by the MDD, may also provide the desired abstraction level during the development of MASs. For this reason, MDD of autonomous agents and MASs has been recognized and become one of the research topics in agent-oriented software engineering (AOSE) area. Contributions are mainly based on the model-driven architecture (MDA), which is the most famous and in-use realization of MDD. Within this direction, AOSE researchers define MAS metamodels in various abstraction levels and apply model transformations between the instances of these metamodels in order to provide rapid and efficient implementation of the MASs in various platforms. Reorganization of the existing MAS development methodologies to support model-driven agent development is another emerging research track. In this paper, we give a state of the art survey on above mentioned model-driven MAS development research activities and evaluate the introduced approaches according to five quality criteria we define on model-driven MAS engineering: (1) definition of a platform independent MAS metamodel, (2) model-to-model transformability, (3) model-to-code transformability, (4) support for multiple MAS platforms and finally (5) tool support for software modeling and code generation. Our evaluation has shown that the researchers contributed to the area by providing MDD processes in which design of the MASs are realized at a very high abstraction level and the software for these MASs are developed as a result of the application of a series of model transformations. However, most of the approaches are incapable of supporting multiple MAS environments due to the restricted specifications of their metamodels and model transformations. Also efficiency and practicability of the proposed methodologies are under debate since the amount and quality of the executable MAS components, gained automatically, appear to be not sufficient.
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Maithripala, D. H. A., Suhada Jayasuriya, and Mark J. Mears. "Phantom Track Generation Through Cooperative Control of Multiple ECAVs Based on Feasibility Analysis." Journal of Dynamic Systems, Measurement, and Control 129, no. 5 (January 22, 2007): 708–15. http://dx.doi.org/10.1115/1.2764512.
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Radar deception through phantom track generation using multiple electronic combat air vehicles is addressed, which serves as a motivating example for cooperative control of autonomous multiagent systems. A general framework to derive sufficient conditions for the existence of feasible solutions for an affine nonlinear control system comprising of a team of nonholonomic mobile agents having to satisfy actuator and interagent constraints is presented. Based on this feasibility analysis, an algorithm capable of generating trajectories online and in real time, for the phantom track generation problem, is developed. A rigorous treatment of the phantom track generation problem, which includes results on its accessibility, feasibility, local asymptotic straightening of trajectories, and a limited result on system controllability, is given. The basic approach to the algorithm based on the results developed here is presented along with simulation results, validating the proposed approach.
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Elkhider, Siddig M., Omar Al-Buraiki, and Sami El-Ferik. "Publish and Subscribe-Based Formation and Containment Control of Heterogeneous Robotic System with Actuator Time Delay." Applied Sciences 11, no. 19 (October 1, 2021): 9145. http://dx.doi.org/10.3390/app11199145.
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This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obstacles. The data exchange between all the agents is provided through the data distribution services (DDS) middleware, which solves the interoperability issue when dealing with heterogeneous multiagent systems. The modified L1 controller is a local controller for stabilizing the dynamic model of each UAV and AUV, and the flocking approach is used to coordinate the followers around the leader or within the space delimited by their leaders. Potential Field (PF) allows obstacle avoidance during the agents’ movement. The performance of the proposed approach under the considerations mentioned above are verified and demonstrated using simulations.
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Vasirani, M., and S. Ossowski. "A Market-Inspired Approach for Intersection Management in Urban Road Traffic Networks." Journal of Artificial Intelligence Research 43 (April 24, 2012): 621–59. http://dx.doi.org/10.1613/jair.3560.
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Traffic congestion in urban road networks is a costly problem that affects all major cities in developed countries. To tackle this problem, it is possible (i) to act on the supply side, increasing the number of roads or lanes in a network, (ii) to reduce the demand, restricting the access to urban areas at specific hours or to specific vehicles, or (iii) to improve the efficiency of the existing network, by means of a widespread use of so-called Intelligent Transportation Systems (ITS). In line with the recent advances in smart transportation management infrastructures, ITS has turned out to be a promising field of application for artificial intelligence techniques. In particular, multiagent systems seem to be the ideal candidates for the design and implementation of ITS. In fact, drivers can be naturally modelled as autonomous agents that interact with the transportation management infrastructure, thereby generating a large-scale, open, agent-based system. To regulate such a system and maintain a smooth and efficient flow of traffic, decentralised mechanisms for the management of the transportation infrastructure are needed. In this article we propose a distributed, market-inspired, mechanism for the management of a future urban road network, where intelligent autonomous vehicles, operated by software agents on behalf of their human owners, interact with the infrastructure in order to travel safely and efficiently through the road network. Building on the reservation-based intersection control model proposed by Dresner and Stone, we consider two different scenarios: one with a single intersection and one with a network of intersections. In the former, we analyse the performance of a novel policy based on combinatorial auctions for the allocation of reservations. In the latter, we analyse the impact that a traffic assignment strategy inspired by competitive markets has on the drivers' route choices. Finally we propose an adaptive management mechanism that integrates the auction-based traffic control policy with the competitive traffic assignment strategy.
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Hanna, Josiah P., Siddharth Desai, Haresh Karnan, Garrett Warnell, and Peter Stone. "Grounded action transformation for sim-to-real reinforcement learning." Machine Learning 110, no. 9 (May 13, 2021): 2469–99. http://dx.doi.org/10.1007/s10994-021-05982-z.
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AbstractReinforcement learning in simulation is a promising alternative to the prohibitive sample cost of reinforcement learning in the physical world. Unfortunately, policies learned in simulation often perform worse than hand-coded policies when applied on the target, physical system. Grounded simulation learning (gsl) is a general framework that promises to address this issue by altering the simulator to better match the real world (Farchy et al. 2013 in Proceedings of the 12th international conference on autonomous agents and multiagent systems (AAMAS)). This article introduces a new algorithm for gsl—Grounded Action Transformation (GAT)—and applies it to learning control policies for a humanoid robot. We evaluate our algorithm in controlled experiments where we show it to allow policies learned in simulation to transfer to the real world. We then apply our algorithm to learning a fast bipedal walk on a humanoid robot and demonstrate a 43.27% improvement in forward walk velocity compared to a state-of-the art hand-coded walk. This striking empirical success notwithstanding, further empirical analysis shows that gat may struggle when the real world has stochastic state transitions. To address this limitation we generalize gat to the stochasticgat (sgat) algorithm and empirically show that sgat leads to successful real world transfer in situations where gat may fail to find a good policy. Our results contribute to a deeper understanding of grounded simulation learning and demonstrate its effectiveness for applying reinforcement learning to learn robot control policies entirely in simulation.
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Alqurashi, Raghda, and Tom Altman. "Hierarchical Agent-Based Modeling for Improved Traffic Routing." Applied Sciences 9, no. 20 (October 16, 2019): 4376. http://dx.doi.org/10.3390/app9204376.
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Agent-based model (ABM) simulation is a bottom–up approach that can describe the phenomena generated from actions and interactions within a multiagent system. An ABM is an improvement over model simulations which only describe the global behavior of a system. Therefore, it is an appropriate technology to analyze emergent phenomena in social sciences and complex adaptive systems such as vehicular traffic and pedestrian crowds. In this paper, a hybrid agent-based modeling framework designed to automate decision-making processes during traffic congestion is proposed. The model provides drivers with real-time alternative routes, computed via a decentralized multi-agent model, that tries to achieve a system-optimal traffic distribution within an entire system, thus reducing the total travel time of all the drivers. The presented work explores a decentralized ABM technique on an autonomous microgrid that is represented through cellular automata (CA). The proposed model was applied to high-density traffic congestion events such as car accidents or lane closures, and its effectiveness was analyzed. The experimental results confirm the efficiency of the proposed model in not only accurately simulating the driver behaviors and improving vehicular traffic flows during congestion but also by suggesting changes to traffic dynamics during the simulations, such as avoiding obstacles and high-density areas and then selecting the best alternative routes. The simulation results validate the ability of the proposed model and the included decision-making sub-models to both predict and improve the behaviors and intended actions of the agents.
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Guessoum, Z. "Adaptive agents and multiagent systems." IEEE Distributed Systems Online 5, no. 7 (2004): 1–4. http://dx.doi.org/10.1109/mdso.2004.10.
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Ahmed, Moamin, Mohd Sharifuddin Ahmad, and Mohd Zaliman M. Yusoff. "A Collaborative Framework for Multiagent Systems." International Journal of Agent Technologies and Systems 3, no. 4 (October 2011): 1–18. http://dx.doi.org/10.4018/jats.2011100101.
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In this paper, the authors demonstrate the use of software agents to extend the role of humans in a collaborative work process. The extended roles to agents provide a convenient means for humans to delegate mundane tasks to software agents. The framework employs the FIPA ACL communication protocol which implements communication between agents. An interface for each agent implements the communication between humans and agents. Such interface and the subsequent communication performed by agents and between agents contribute to the achievement of shared goals.
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WEYNS, DANNY, MICHAEL SCHUMACHER, ALESSANDRO RICCI, MIRKO VIROLI, and TOM HOLVOET. "Environments in multiagent systems." Knowledge Engineering Review 20, no. 2 (June 2005): 127–41. http://dx.doi.org/10.1017/s0269888905000457.
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There is a growing awareness in the multiagent systems research community that the environment plays a prominent role in multiagent systems. Originating from research on behavior-based agent systems and situated multiagent systems, the importance of the environment is now gradually being accepted in the multiagent system community in general. In this paper, we put forward the environment as a first-order abstraction in multiagent systems. This position is motivated by the fact that several aspects of multiagent systems that conceptually do not belong to agents themselves should not be assigned to, or hosted inside the agents. Examples are infrastructure for communication, the topology of a spatial domain or support for the action model. These and other aspects should be considered explicitly. The environment is the natural candidate to encapsulate these aspects. We elaborate on environment engineering, and we illustrate how the environment plays a central role in a real-world multiagent system application.
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Wood, Jared, and J. Karl Hedrick. "Partition Learning for Multiagent Planning." Journal of Robotics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/590479.
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Automated surveillance of large geographic areas and target tracking by a team of autonomous agents is a topic that has received significant research and development effort. The standard approach is to decompose this problem into two steps. The first step is target track estimation and the second step is path planning by optimizing directly over target track estimation. This standard approach works well in many scenarios. However, an improved approach is needed for the scenario when general, nonparametric estimation is required, and the number of targets is unknown. The focus of this paper is to present a new approach that inherently handles the task to search for and track anunknownnumber of targets within alargegeographic area. This approach is designed for the case when the search is performed by a team of autonomous agents and target estimation requires general, nonparametric methods. There are consequently very few assumptions made. The only assumption made is that a time-changing target track estimation is available and shared between the agents. This estimation is allowed to be general and nonparametric. Results are provided that compare the performance of this new approach with the standard approach. From these results it is concluded that this new approach improves search and tracking when the number of targets is unknown and target track estimation is general and nonparametric.
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Stipanović, Dušan M., Peter F. Hokayem, Mark W. Spong, and Dragoslav D. Šiljak. "Cooperative Avoidance Control for Multiagent Systems." Journal of Dynamic Systems, Measurement, and Control 129, no. 5 (April 27, 2007): 699–707. http://dx.doi.org/10.1115/1.2764510.
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The objective of this paper is to present a methodology for designing cooperative control laws for individual agents that guarantee collision avoidance in multiagent systems. The proposed avoidance control laws are easy to design and implement, and may be directly appended to the optimal control laws of the individual agents within the cooperation framework. The avoidance control laws are computed using value functions that resemble the behavior of barrier functions in the static optimization theory. The most attractive feature of the proposed optimization scheme is the fact that the avoidance laws are active only in the bounded sensing regions of each individual agent, and they do not interfere with the agents’ individual optimal control laws outside of these regions.
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SHAKERI, CIRRUS, and DAVID C. BROWN. "Constructing design methodologies using multiagent systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 2 (May 2004): 115–34. http://dx.doi.org/10.1017/s0890060404040090.
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An innovative approach has been developed for discovering better design methodologies that is based on simulating the design process using a multiagent system that mimics the behavior of a design team. The system implements a knowledge-based model of design in which highly specialized knowledge from expert sources is applied to synthesize a design. The agents activate the pieces of design knowledge when they become applicable. The use of knowledge by agents is recorded by tracing the steps that the agents have taken during a design project. Many traces are generated by solving a large number of design projects that differ in their requirements. A set of design methodologies is constructed by using inductive learning techniques to generalize the traces generated. These methodologies then can be used to guide human design teams through future design projects.
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Zou, Yi, Jijuan Zhong, Zhihao Jiang, Hong Zhang, and Xuyu Pu. "Experience Weighted Learning in Multiagent Systems." Scientific Programming 2021 (November 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/9948156.
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Agents face challenges to achieve adaptability and stability when interacting with dynamic counterparts in a complex multiagent system (MAS). To strike a balance between these two goals, this paper proposes a learning algorithm for heterogeneous agents with bounded rationality. It integrates reinforcement learning as well as fictitious play to evaluate the historical information and adopt mechanisms in evolutionary game to adapt to uncertainty, which is referred to as experience weighted learning (EWL) in this paper. We have conducted multiagent simulations to test the performance of EWL in various games. The results demonstrate that the average payoff of EWL exceeds that of the baseline in all 4 games. In addition, we find that most of the EWL agents converge to pure strategy and become stable finally. Furthermore, we test the impact of 2 import parameters, respectively. The results show that the performance of EWL is quite stable and there is a potential to improve its performance by parameter optimization.
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Burkov, Andriy, and Brahim Chaib-Draa. "Repeated games for multiagent systems: a survey." Knowledge Engineering Review 29, no. 1 (March 18, 2013): 1–30. http://dx.doi.org/10.1017/s026988891300009x.
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AbstractRepeated games are an important mathematical formalism to model and study long-term economic interactions between multiple self-interested parties (individuals or groups of individuals). They open attractive perspectives in modeling long-term multiagent interactions. This overview paper discusses the most important results that actually exist for repeated games. These results arise from both economics and computer science. Contrary to a number of existing surveys of repeated games, most of which originated from the economic research community, we are first to pay a special attention to a number of important distinctive features proper to artificial agents. More precisely, artificial agents, as opposed to the human agents mainly aimed by the economic research, are usually bounded whether in terms of memory or performance. Therefore, their decisions have to be based on the strategies defined using finite representations. Furthermore, these strategies have to be efficiently computed or approximated using a limited computational resource usually available to artificial agents.
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LIU, JIMING, and HAN JING. "ALIFE: A MULTIAGENT COMPUTING PARADIGM FOR CONSTRAINT SATISFACTION PROBLEMS." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 03 (May 2001): 475–91. http://dx.doi.org/10.1142/s0218001401000988.
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This paper presents a new approach to solving N-queen problems, which involves a model of distributed autonomous agents with artificial life (ALIFE) and a method of representing N-queen constraints in an agent environment. The distributed agents locally interact with their living environment, i.e. a chessboard, and execute their reactive behaviors by applying their behavioral rules for randomized motion, least-conflict position searching, and cooperating with other agents, etc. The agent-based N-queen problem solving system evolves through selection and contest, in which some agents will die or be eaten if their moving strategies are less effective than others. The experimental results have shown that this system is capable of solving large-scale N-queen problems. This paper also provides a model of ALIFE agents for solving general CSPs.
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Cubillos, Claudio, Makarena Donoso, Nibaldo Rodríguez, Franco Guidi-Polanco, and Daniel Cabrera-Paniagua. "Towards Open Agent Systems Through Dynamic Incorporation." International Journal of Computers Communications & Control 5, no. 5 (December 1, 2010): 675. http://dx.doi.org/10.15837/ijccc.2010.5.2223.
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This work tackles the problem of providing a mechanism and infrastructure for allowing a given Multiagent System (MAS) to become open, allowing the incorporation of newly incoming agents to participate within the existing society. For this, a conceptual analysis of the so-called conciliation problem is presented, covering the diverse levels and issues involved in such a process. Our Dynamic Incorporation Architecture is presented, which implements an infrastructure for allowing the participation of external agents into a specific multiagent system by incorporating the appropriate behaviours upon arrival. Our multiagent architecture for dynamic incorporation covers three levels: semantics, communication and interaction and has been applyed in a book-trading e-market scenario.
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Telang, Pankaj, Munindar P. Singh, and Neil Yorke-Smith. "Maintenance of Social Commitments in Multiagent Systems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11369–77. http://dx.doi.org/10.1609/aaai.v35i13.17355.
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We introduce and formalize a concept of a maintenance commitment, a kind of social commitment characterized by states whose truthhood an agent commits to maintain. This concept of maintenance commitments enables us to capture a richer variety of real-world scenarios than possible using achievement commitments with a temporal condition. By developing a rule-based operational semantics, we study the relationship between agents' achievement and maintenance goals, achievement commitments, and maintenance commitments. We motivate a notion of coherence which captures alignment between an agents' achievement and maintenance cognitive and social constructs, and prove that, under specified conditions, the goals and commitments of both rational agents individually and of a multiagent system are coherent.
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Mahmoud, Moamin A., Mohd Sharifuddin Ahmad, Mohd Zaliman Mohd Yusoff, and Aida Mustapha. "A Review of Norms and Normative Multiagent Systems." Scientific World Journal 2014 (2014): 1–23. http://dx.doi.org/10.1155/2014/684587.
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Norms and normative multiagent systems have become the subjects of interest for many researchers. Such interest is caused by the need for agents to exploit the norms in enhancing their performance in a community. The term norm is used to characterize the behaviours of community members. The concept of normative multiagent systems is used to facilitate collaboration and coordination among social groups of agents. Many researches have been conducted on norms that investigate the fundamental concepts, definitions, classification, and types of norms and normative multiagent systems including normative architectures and normative processes. However, very few researches have been found to comprehensively study and analyze the literature in advancing the current state of norms and normative multiagent systems. Consequently, this paper attempts to present the current state of research on norms and normative multiagent systems and propose a norm’s life cycle model based on the review of the literature. Subsequently, this paper highlights the significant areas for future work.
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Sun, Tairen, Yongping Pan, and Haoyong Yu. "Leader-Based Consensus of Heterogeneous Nonlinear Multiagent Systems." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/519524.
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This paper considers the leader-based consensus of heterogeneous multiple agents with nonlinear uncertain systems. Based on the information obtained from the following agents’ neighbors, leader observers are designed by the following agents to estimate the leader’s states and nonlinear dynamics. Then, to achieve leader-based consensus, adaptive distributed controllers are designed for the following agents to track the designed corresponding leader observers. The effectiveness of the leader observers and distributed consensus controllers are illustrated by formal proof and simulation results.
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Yang, Hongyong, Fujun Han, Fei Liu, Huixia Liu, and Mei Zhao. "Distributed Coordination of Fractional Dynamical Systems with Exogenous Disturbances." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/793903.
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Distributed coordination of fractional multiagent systems with external disturbances is studied. The state observer of fractional dynamical system is presented, and an adaptive pinning controller is designed for a little part of agents in multiagent systems without disturbances. This adaptive pinning controller with the state observer can ensure multiple agents' states reaching an expected reference tracking. Based on disturbance observers, the controllers are composited with the pinning controller and the state observer. By applying the stability theory of fractional order dynamical systems, the distributed coordination of fractional multiagent systems with external disturbances can be reached asymptotically.
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Pierzchała, Dariusz, and Przemysław Czuba. "Method of agents’ state estimation in multiresolution multiagent simulation." Computer Science and Mathematical Modelling, no. 8/2018 (March 25, 2019): 29–39. http://dx.doi.org/10.5604/01.3001.0013.1460.
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The paper proposes the multiagent techniques for approximation of agent’s state in the multiresolution multiagent simulation. The key methods we have used for state aggregation and disaggregation are: consensus algorithm and formation control. The idea of the coordination of multiple agents has emerged from both observation and simulation of a collective behavior of biological entities. The consensus algorithms are commonly used for the cooperative control problems in the multiagent systems, whilst the formation control is the most popular and fundamental motion coordination problem in the multiagent systems, where agents converge to predefined geometric shapes. The presented approach shows that multiagent methods seem to be very promising in multiresolution simulation. Consensus and formation control algorithms remove necessity to specify the much more complex algorithms for the aggregation and disaggregation needs.
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van Katwijk, R. T., P. van Koningsbruggen, B. De Schutter, and J. Hellendoorn. "Test Bed for Multiagent Control Systems in Road Traffic Management." Transportation Research Record: Journal of the Transportation Research Board 1910, no. 1 (January 2005): 108–15. http://dx.doi.org/10.1177/0361198105191000113.
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A test bed for multiagent control systems in road traffic management is presented. As the complexity of traffic control on a network grows, it becomes more difficult to coordinate the actions of the large number of heterogeneous traffic management instruments that are available in the network. One way of handling this complexity is to divide the coordination problem into smaller coherent subproblems that can be solved with a minimum of interaction. Multiagent systems can aid in the distribution of the problem (over the various agents that compose the multiagent system) and facilitate the coordination of the activities of these agents when required. In the literature, no consensus exists about the best configuration of the traffic-managing multiagent system and how the activities of the agents that compose the multiagent system should be coordinated. The decomposition of a problem into various subproblems is an active field of research in the world of distributed artificial intelligence. A survey of approaches reported in the literature is presented. Subsequently, both the test bed and the modules that compose it are introduced. Finally, an application is presented that illustrates some of the research the test bed has made possible.
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Omicini, Andrea, and Stefano Mariani. "Agents & multiagent systems: En route towards complex intelligent systems." Intelligenza Artificiale 7, no. 2 (2013): 153–64. http://dx.doi.org/10.3233/ia-130056.
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