Prashant Doshi
ABSTRACT
Interactive dynamic influence diagrams (I-DIDs) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. We formalize the concept of a minimal model set, which facilitates qualitative comparisons between different approximation techniques. We then present a new approximation technique that minimizes the space of candidate models by discriminating between model updates. We empirically demonstrate that our approach improves significantly in performance on the previous clustering based approximation technique.
- P. Doshi, Y. Zeng, and Q. Chen. Graphical models for interactive pomdps: representations and solutions. JAAMAS, DOI:10.1007/s10458-008-9064-7, 2008. Google Scholar
Cross Ref
- Y. Gal and A. Pfeffer. A language for modeling agent's decision-making processes in games. In AAMAS, pages 265--272, 2003. Google ScholarDigital Library
- P. Gmytrasiewicz and P. Doshi. A framework for sequential planning in multiagent settings. JAIR, 24:49--79, 2005. Google ScholarCross Ref
- D. Koller and B. Milch. Multi-agent IDs for representing and solving games. In IJCAI, pages 1027--1034, 2001. Google ScholarDigital Library
- J. Pineau, G. Gordon, and S. Thrun. Anytime point-based value iteration for large pomdps. JAIR, 27:335--380, 2006. Google ScholarDigital Library
- D. Pynadath and S. Marsella. Minimal mental models. In AAAI, pages 1038--1044, 2007. Google ScholarDigital Library
- B. Rathnas., P. Doshi, and P. J. Gmytrasiewicz. Exact solutions to interactive pomdps using behavioral equivalence. In AAMAS, pages 1025--1032, 2006. Google ScholarDigital Library
- S. Seuken and S. Zilberstein. Improved memory bounded dynamic programming for decentralized pomdps. In UAI, pages 2009--2015, 2007. Google ScholarDigital Library
- R. Smallwood and E. Sondik. The optimal control of partially observable markov decision processes over a finite horizon. OR, 21:1071--1088, 1973.Google ScholarDigital Library
- D. Suryadi and P. Gmytrasiewicz. Learning models of other agents using IDs. In UM, pages 223--232, 1999. Google ScholarDigital Library
- J. A. Tatman and R. D. Shachter. Dynamic programming and influence diagrams. IEEE Trans. SMC, 20(2):365--379, 1990.Google ScholarCross Ref
Index Terms
- Improved approximation of interactive dynamic influence diagrams using discriminative model updates
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