ABSTRACT
In many cooperative multiagent domains, there exist some states in which the agents can act independently and others in which they need to coordinate with their teammates. In this paper, we explore how factored representations of state can be used to generate factored policies that can, with minimal communication, be executed distributedly by a multiagent team. The factored policies indicate those portions of the state where no coordination is necessary, automatically alert the agents when they reach a state in which they do need to coordinate, and determine what the agents should communicate in order to achieve this coordination. We evaluate the success of our approach experimentally by comparing the amount of communication needed by a team executing a factored policy to a team that needs to communicate in every timestep.
- D. S. Bernstein, R. Given, N. Immerman, and S. Zilberstein. The complexity of centralized control of Markov decision processes. Mathematics of Operations Research, 2002. Google ScholarDigital Library
- C. Boutilier, R. Dearden, and M. Goldszmidt. Stochastic dynamic programming with factored representations. Artificial Intelligence, 2000. Google ScholarDigital Library
- C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller. Context-specific independence in Bayesian networks. In Uncertainty in Artificial Intelligence, 1996. Google ScholarDigital Library
- T. Dean and K. Kanazawa. A model for reasoning about persistence and causation. Computational Intelligence Journal, 1989. Google ScholarDigital Library
- C. V. Goldman and S. Zilberstein. Optimizing information exchange in cooperative multi-agent systems. In International Joint Conferences on Autonomous Agents and multi-Agent Systems, 2003. Google ScholarDigital Library
- C. V. Goldman and S. Zilberstein. Decentralized control of cooperative systems: Categorization and complexity analysis. Journal of AI Research, 2004. Google ScholarDigital Library
- C. Guestrin and G. Gordon. Distributed planning in hierarchical factored MDPs. In Uncertainty in Artificial Intelligence, 2002. Google ScholarDigital Library
- C. Guestrin, S. Venkataraman, and D. Koller. Context specific multiagent coordination and planning with factored MDPs. In AAAI Spring Symposium, 2002. Google ScholarDigital Library
- E. Hansen and Z. Feng. Dynamic programming for POMDPs using a factored state representation. In International Conference on AI Planning Systems, 2000.Google Scholar
- J. Hoey, R. St-Aubin, A. Hu, and C. Boutilier. SPUDD: Stochastic planning using decision diagrams. In Uncertainty in Artificial Intelligence, 1999. Google ScholarDigital Library
- R. Nair, M. Roth, M. Yokoo, and M. Tambe. Communication for improving policy computation in distributed POMDPs. In International Joint Conferences on Autonomous Agents and multi-Agent Systems, 2004. Google ScholarDigital Library
- D. V. Pynadath and M. Tambe. The communicative Multiagent Team Decision Problem: Analyzing teamwork theories and models. Journal of AI Research, 2002. Google ScholarDigital Library
- M. Roth, R. Simmons, and M. Veloso. Reasoning about joint beliefs for execution-time communication decisions. In International Joint Conferences on Autonomous Agents and multi-Agent Systems, 2005. Google ScholarDigital Library
Index Terms
- Exploiting factored representations for decentralized execution in multiagent teams
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