Approximate solutions of interactive dynamic influence diagrams using model clustering

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-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. Copyright © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.