论文信息 - How to design good learning agents in organization

How to design good learning agents in organization

This paper categorizes four types of multiagent learning in terms of both goals and evaluations in agents, and investigates the characteristics of each categorization to find an effective type for designing learning agents. Since the characteristics in this categorization are affected by the learning mechanisms of agents, the characteristics are investigated by referring to organizational learning in organization and management science as one of methods. Through intensive simulations on a complex domain problem, the following implication has been revealed: agents that pursue their own goals and are evaluated according to their total results show high performance in comparison with other types of agents when all learning mechanisms in the organizational learning are employed.

[1] Sandip Sen,et al. Adaption and Learning in Multi-Agent Systems , 1995, Lecture Notes in Computer Science.

[2] John H. Holland,et al. Cognitive systems based on adaptive algorithms , 1977, SGAR.

[3] Takao Terano,et al. Multiagent reinforcement learning with organizational-learning oriented classifier system , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4] Pat Langley,et al. Learning distributed strategies for traffic control , 1998 .

[5] Alexis Drogoul,et al. Agent Oriented Design of a Soccer Robot Team , 1996 .

[6] Daniel H. Kim. The Link between individual and organizational learning , 1997 .

[7] John H. Holland,et al. COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS1 , 1978 .

[8] Tucker R. Balch,et al. Communication in reactive multiagent robotic systems , 1995, Auton. Robots.

[9] Ming Tan,et al. Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents , 1997, ICML.

[10] Takao Terano,et al. Making Organizational Learning Operational: Implications from Learning Classifier Systems , 1999, Comput. Math. Organ. Theory.

[11] Richard S. Sutton,et al. Introduction to Reinforcement Learning , 1998 .

[12] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .