of learning and adaptation in multi-agent systems has been
given increasing attention in artificial intelligence research.
It is becoming clear, given the dynamic environments in which
we want our agent teams to interact, that behavioral repertoires
and activities cannot simply be defined in advance. Our approach
to multi-agent learning, unlike the top-down model of assuming
an agent's state in advance, is notable for its similarity
to the types of learning exhibited by lower animal societies.
goal is to enable multiple agents to learn a cooperative and
coordinated behavior in a dynamic environment using reinforcement
agent does not have any prior knowledge.
agent is a self-interested entity and behaves to achieve
maximum reward in the range of its knowledge of the environment.
is Profit Sharing Plan (PSP), which is a type of reinforcement
learning algorithm. The PSP algorithm allows an autonomous
agent to learn a behavior progressively without any instruction
and only with delayed rewards. PSP differs from other approaches
to learning (like Markov Decision Processes) in that it does
not assume an agent's state in advance.
Ankolekar, Y. W. Seo, and K. Sycara, "Investigating
Semantic Knowledge for Text Learning," ACM SIGIR-2003
Workshop on Semantic Web, Toronto, Canada, August 1,
Huang and K. Sycara, "Multi-agent Learning in Extensive
Games with Complete Information". In Proceedings of the
Second International Conference on Autonomous Agents and
Multiagent Systems, Melbourne, Australia, July 14-19,
Glickman and K. Sycara, "Evolutionary Search, Stochastic
Policies with Memory, and Reinforcement Learning with Hidden
Proceedings of the Eighteenth International Conference on
Machine Learning, 2002.
Zeng and K. Sycara, "Effects of Learning in Negotiation,"
in Encyclopedia of Computer Science and Technology,
Allen Kent and James Williams (eds), Vol 44, pp. 15-33,
Marcel Dekker Inc., 2001.
- S. Arai and
K. Sycara, "Credit
Assignment Method for Learning Effective Stochastic Policies
in Uncertain Domains," Proceedings of Genetic
and Evolutionary Computation Conference (GECCO-2001),
- S. Arai, K.
Sycara, and T. R. Payne, "Experience-based
Reinforcement Learning to Acquire Effective Behavior in
a Multi-agent Domain," Proceedings of the 6th
Pacific Rim International Conference on Artificial Intelligence,
Lecture Notes in Artificial Intelligence 1886, Springer-Verlag,
- Arai, S., and
Sycara, K., Effective Learning
Approach for Planning and Scheduling in Multi-Agent Domain,
Proceedings of the 6th International Conference on Simulation
of Adaptive Behavior (From animals to animats 6), pp.507-516
Arai, K. Sycara and T. R.Payne
"Multi-agent Reinforcement Learning for Scheduling Multiple-Goals,"
in Proceedings of the Fourth International Conference
on Multi-Agent Systems (ICMAS'2000).
Arai, S., and K. Sycara, "Effective
Learning Approach for Planning and Scheduling in Multi-Agent
Domain." In Proceedings of the 6th International
Conference on Simulation of Adaptive Behavior (From animals
to animats 6), pp.507-516 (2000).
Zeng, and K. Sycara, "Bayesian Learning in Negotiation,"
in International Journal of Human Computer Systems,
Vol. 48, pp.125-141, 1998.
and K. Sycara, "Benefits
of Learning in Negotiation," in Proceedings
of AAAI-97 (in pdf)
Sycara, and A. Pannu, "A
Learning Personal Agent for Text Filtering and Notification,"
in Proceedings of the International Conference of Knowledge-Based
Systems (KBCS 96), Dec. 1996. (in pdf)
Sycara and A. Pannu, "Learning
Text Filtering Preferences," Symposium on Machine
Learning And Information Access. AAAI 96 Symposium Series,
Mar. 1996, Stanford, CA. Figure 2. (in pdf)
Sycara and K. Miyashita, "Learning Control Knowledge through
Case-Based Acquisition of User Optimization Preferences,"
in Knowledge Acquisition and Machine Learning: An Integrated
Approach, Y. Kodratoff and G. Tecuci (eds), Morgan Kaufman
Sycara, "Machine Learning for Intelligent Support
of Conflict Resolution," in Decision Support Systems,
Vol. 10, pp.121-136, 1993.
For more information on multi-agent learning, a summary of
the PSP method, and some experimental results, see the following