Market-Driven Multi-Agent Collaboration in Robot Soccer Domain

Köse, Hatice; Kaplan, Kemal; Meriçli, Çetin; Tatlidede, Utku; Akın, H. Levent

doi:10.5772/4661

Cited by 32 publications

(37 citation statements)

References 14 publications

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“…In order to let the team develop some defensive skills, the team was trained against the BrianTeam team, the codes of which come with the TeamBots simulator. Finally, the robots were trained against a strong team named MarketTeam, which uses a market-driven role allocation algorithm and a similar potential field approach in which the coefficients of the field forces are trained by using Genetic Algorithms (GA) [19], [20]. Being a strong and offensive team, MarketTeam forced our team to learn some defensive behavior.…”

Section: Methodsmentioning

confidence: 99%

“…The algorithms were implemented and tested on the TeamBots mobile robot simulator environment [18]. Köse et al [19], [20] presented a task allocation algorithm based on free-market approach, in which the players evaluate their fitnesses to each role by calculating a cost function for accomplishing the main task of that role. These costs are exchanged among teammates and whichever player offers the lowest cost for a role is assigned to that role.…”

Section: B Learning Large Scale Tasksmentioning

confidence: 99%

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Soccer without intelligence

Meriçli

Akın

2009

2008 IEEE International Conference on Robotics and Biomimetics

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Abstract-Robot soccer is an excellent testbed to explore innovative ideas and test the algorithms in multi-agent systems (MAS) research. A soccer team should play in an organized manner in order to score more goals than the opponent, which requires well-developed individual and collaborative skills, such as dribbling the ball, positioning, and passing. However, none of these skills needs to be perfect and they do not require highly complicated models to give satisfactory results. This paper proposes an approach inspired from ants, which are modeled as Braitenberg vehicles for implementing those skills as combinations of very primitive behaviors without using explicit communication and role assignment mechanisms, and applying reinforcement learning to construct the optimal stateaction mapping. Experiments demonstrate that a team of robots can indeed learn to play soccer reasonably well without using complex environment models and state representations. After very short training sessions, the team started scoring more than its opponents that use complex behavior codes, and as a result of having very simple state representation, the team could adapt to the strategies of the opponent teams during the games.

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Section: Methodsmentioning

confidence: 99%

Section: B Learning Large Scale Tasksmentioning

confidence: 99%

Soccer without intelligence

Meriçli

Akın

2009

2008 IEEE International Conference on Robotics and Biomimetics

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“…To develop the market-driven strategy, a cost function must be defined for all of the resources needed to accomplish the task. Each of these resources can have a different weight depending on the importance of each to perform the task [20].…”

Section: A Market-driven Coordinationmentioning

confidence: 99%

“…The idea of methodoriented MASs is based on the interaction of agents in a distributed manner, which allows agents to have greater bargaining power and information. Thus, agents must have a mechanism that allows for communication amongst them [20].…”

Section: A Market-driven Coordinationmentioning

confidence: 99%

Intelligent Multiagent Coordination Based On Neuro-Fuzzy Models With Hierarchical Reinforcement Learning

Forero¹,

Vellasco²,

Figueiredo³

et al. 2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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Abstract-This paper presents the research and development of a hybrid neuro-fuzzy model for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent neuro-fuzzy multiagent systems that use MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and a MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-GC). After setting options and using the MA-RL-HNFP_MA family of models, the coordination systems were tested in two case studies involving the implementation of a benchmark game of predator-prey. The tests showed that the new system has the ability to coordinate actions between agents with a convergence rate nearly 30% greater than that of the original version.

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“…A significant amount of recent work addresses more powerful market-based approaches for task allocation [39], [58], [96], [117], [61], [11], [91], [127], [42], [52], [64], [32], [50], [85]. These approaches can deal with more complex task representations, more coupling between tasks, dynamic events, combinatorial auctions, and so forth.…”

Section: B Organizational Approach To Task Allocationmentioning

confidence: 99%

Distributed intelligence: overview of the field and its application in multi-robot systems

Parker¹

2008

jopha

151

107

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Abstract-This article overviews the concepts of distributed intelligence, outlining the motivations for studying this field of research. First, common systems of distributed intelligence are classified based upon the types of interactions exhibited, since the type of interaction has relevance to the solution paradigm to be used. We outline three common paradigms for distributed intelligence -the bioinspired paradigm, the organizational and social paradigm, and the knowledge-based, ontological paradigm -and give examples of how these paradigms can be used in multi-robot systems. We then look at a common problem in multirobot systems -that of task allocation -and show how the solution approach to this problem is very different depending upon the paradigm chosen for abstracting the problem. Our conclusion is that the paradigms are not interchangeable, but rather the selection of the appropriate paradigm is dependent upon the specific constraints and requirements of the application of interest. Further work is needed to provide guidance to the system designer on selecting the proper abstraction, or paradigm, for a given problem.

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Market-Driven Multi-Agent Collaboration in Robot Soccer Domain

Cited by 32 publications

References 14 publications

Soccer without intelligence

Soccer without intelligence

Intelligent Multiagent Coordination Based On Neuro-Fuzzy Models With Hierarchical Reinforcement Learning

Distributed intelligence: overview of the field and its application in multi-robot systems

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