Approximate solutions of interactive dynamic influence diagrams using model clustering

Authors:
Yifeng Zeng;Prashant Doshi;Qiongyu Chen
Affiliations:
Dept. of Computer Science, Aalborg University, Aalborg, Denmark;Dept. of Computer Science, University of Georgia, Athens, GA;Dept. of Computer Science, National Univ. of Singapore, Singapore
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Year:
2007

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Abstract

Interactive dynamic influence diagrams (I-DIDs) offer a transparent and semantically clear representation for the sequential decision-making problem over multiple time steps in the presence of other interacting agents. Solving I-DlDs exactly involves knowing the solutions of possible models of the other agents, which increase exponentially with the number of time steps. We present a method of solving I-DlDs approximately by limiting the number of other agents' candidate models at each time step to a constant. We do this by clustering the models and selecting a representative set from the clusters. We discuss the error bound of the approximation technique and demonstrate its empirical performance.