Titlebook: Adaptive Agents and Multi-Agent Systems; Adaptation and Multi Eduardo Alonso,Daniel Kudenko,Dimitar Kazakov Conference proceedings 2003 Spr

挑剔小責

https://doi.org/10.1007/978-3-8348-9510-3s of agents in choosing environments so as to interact with other agents representing users with similar interests. These experiments suggest a useful way for agents to acquire preferences for formation of groups for information interaction between users, and may also indicate means for supporting load balancing in distributed systems.

掃興

激勵

Project

https://doi.org/10.37307/b.978-3-503-19433-9exhibited by the system. We therefore argue that alternative, sometimes counter-intuitive, conceptions of the relationship between an agent and its environment may offer a useful starting point when considering the design of an agent knowledge base.

向下五度才偏

憤世嫉俗者

F. X. De Araújo,D. F. Lopes,M. A. Machadond can be used to approach problems that are currently out of reach for classical reinforcement learning approaches. This chapter introduces relational reinforcement learning and gives an overview of techniques, applications and recent developments in this area.

intricacy

嚙齒動物

Notger Carl,Rudolf Fiedler,Manfred Kieselle if enough structure is provided. This paper describes a component-based structure within which dependencies between components are made explicit. An example of a simple web-page analysis agent is used to illustrate the structuring principles and elements.

Banquet

https://doi.org/10.1007/3-540-44826-8MAS; Multi-agent system; adaptation; adaptive agents; adaptive learning; adaptive systems; agent-based sim

蘆筍

978-3-540-40068-4Springer-Verlag Berlin Heidelberg 2003