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Controlling cooperative problem solving in industrial multi-agent systems using joint intentions.
One reason why Distributed AI (DAI) technology has been deployed in relatively few real-size applications is that it lacks a clear and implementable model of cooperative problem solving which specifies how agents should operate and interact in complex, dynamic and unpredictable environments. As a consequence of the experience gained whilst building a number of DAI systems for industrial applications, a new principled model of cooperation has been developed. This model, called Joint Responsibility, has the notion of joint intentions at its core. It specifies pre-conditions which must be attained before collaboration can commence and prescribes how individuals should behave both when joint activity is progressing satisfactorily and also when it runs into difficulty. The theoretical model has been used to guide the implementation of a general-purpose cooperation framework and the qualitative and quantitative benefits of this implementation have been assessed through a series of comparative experiments in the real-world domain of electricity transportation management. Finally, the success of the approach of building a system with an explicit and grounded representation of cooperative problem solving is used to outline a proposal for the next generation of multi-agent systems.
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Distributed Problem Solving and Multi-Agent Systems: Comparisons and Examples*
- E. Durfee , J. Rosenschein
- Published 1994
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Problem-Solving in Multi-Agent Systems: A Novel Generalized Particle Model
East China University of Sci. and Tech., China
Huazhong University of Sci. and Tech., China
Qingdao Technological University, China
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IMSCCS '06: Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 2 (IMSCCS'06) - Volume 02
This paper presents a novel generalized particle model (GPM) for problem-solving in multi-agent systems (MAS).1 The construction, dynamics and properties of the GPA and corresponding algorithm are discussed. The GPA has many advantages in terms of the high-scale parallelism, multi-objective optimization, multi-type coordination, multi-degree autonomy, and the ability to deal randomly occurring phenomena in MAS systems.
Distributed artificial intelligence
Planning and scheduling
Mathematics of computing
Theory of computation
Design and analysis of algorithms
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IEEE Computer Society
- Published: 20 April 2006
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Special Issue "Multi-Agent Systems Design, Analysis, and Applications"
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A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 4095
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Multiagent systems have received tremendous attention in different disciplines, including computer science, artificial intelligence, civil engineering, medicine, etc. These systems are composed of self-governing and intelligent parts, called agents, which are autonomous, socially intelligent, reactive, and/or pro-active. They interact with each other, situated in a common environment, eventually participating to or building an organization. Each agent decides on a proper action to solve the task using multiple inputs, e.g., history of actions, interactions with other agents, or its own goal.
This Special Issue solicits papers addressing original research on foundations, theory, development, analysis, and applications of multiagent systems composed by autonomous agents. Topics of interest include economic paradigms (cooperative and non-cooperative algorithmic game theory); social choice and voting; mechanism design; cooperation and teamwork; distributed problem solving; coalition formation; agent societies and societal issues; social networks; trust and reputation; ethical and legal issues; privacy, safety and security; and learning (evolutionary algorithms, multiagent learning, reinforcement learning, deep learning).
Dr. Angelo Fanelli Prof. Dr. Gianpiero Monaco Prof. Dr. Luca Moscardelli Guest Editors
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- Economic paradigms
- Algorithmic game theory (cooperative and non-cooperative)
- Social choice and voting
- Mechanism design
- Cooperation and teamwork
- Coalition formation
- Social networks
- Privacy, safety, and security
- Trust and reputation
- Distributed problem solving
- Evolutionary algorithms
- Multiagent learning
- Reinforcement learning
- Deep learning
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Multi-agent systems are a way to model decentralised problem solving (privacy, distribution). Agents, having personal goals and constraints, negotiate as to
Khue, N.T.M. Developing an Intelligent Multi-Agent System based on JADE to solve problems automatically. International Conference on Systems and Informatics (
Finally, the success of the approach of building a system with an explicit and grounded representation of cooperative problem solving is used to outline a
Multi-agent systems are usually very complex in their structure and functionality. In most of the application tasks, it is, difficult or
A multiagent system consists of multiple interacting software components known as agents, which cooperate to solve problems that are infeasible to any
Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve.
120 Citations · Problems of learning in multi-agent systems · Rational Agents, Limited Knowledge, and Nash Equilibria (Extended Abstract) · Cooperative Multiagent
The theoretical problem needed to solve is to optimize the number m * of agent that produces the smallest total cost, assuming that the hiring of additional
Problem-Solving in Multi-Agent Systems: A Novel Generalized Particle Model.
They interact with each other, situated in a common environment, eventually participating to or building an organization. Each agent decides on a proper action