挑剔小责
发表于 2025-3-27 00:21:01
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.
扫兴
发表于 2025-3-27 03:21:03
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激励
发表于 2025-3-27 08:57:28
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Project
发表于 2025-3-27 11:21:37
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
发表于 2025-3-27 17:17:57
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愤世嫉俗者
发表于 2025-3-27 18:17:46
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
发表于 2025-3-28 00:59:36
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啮齿动物
发表于 2025-3-28 06:07:06
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
发表于 2025-3-28 07:18:37
https://doi.org/10.1007/3-540-44826-8MAS; Multi-agent system; adaptation; adaptive agents; adaptive learning; adaptive systems; agent-based sim
芦笋
发表于 2025-3-28 12:28:58
978-3-540-40068-4Springer-Verlag Berlin Heidelberg 2003