Yifeng Zeng, Prashant Doshi, Qiongyu Chen
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-DIDs 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-DIDs 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.
Subjects: 7.1 Multi-Agent Systems; 15.5 Decision Theory
Submitted: Apr 24, 2007