Learning and Adaption in Multi-Agent Systems
First International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers
Book and Conference Proceedings
First International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers
Article
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...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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 ...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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. ...
Chapter and Conference Paper
A competitive distributed recommendation mechanism is introduced based on adaptive software agents for efficiently allocating the “customer attention space”, or banners. In the example of an electronic shoppin...