Learning and Adaption in Multi-Agent Systems
First International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers
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6 Result(s)
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Chapter and Conference Paper
COllective INtelligence with Sequences of Actions
The design of a Multi-Agent System (MAS) to perform well on a collective task is non-trivial. Straightforward application of learning in a MAS can lead to sub optimal solutions as agents compete or interfere. ...
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Chapter and Conference Paper
Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics
In this paper, we show how the dynamics of Q-learning can be visualized and analyzed from a perspective of Evolutionary Dynamics (ED). More specifically, we show how ED can be used as a model for Q-learning in...
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Chapter and Conference Paper
Decommitment in a Competitive Multi-Agent Transportation Setting
Decommitment is the action of foregoing of a contract for another (superior) offer. It has been analytically shown that, using decommitment, agents can reach higher utility levels in case of negotiations with ...
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Book and Conference Proceedings
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Article
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not stationary and the Markov pro...
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Chapter and Conference Paper
An Overview of Cooperative and Competitive Multiagent Learning
Multi-agent systems (MASs) is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexities arising from thei...
