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|Title:||Approximate solutions of interactive dynamic influence diagrams using model clustering|
|Citation:||Zeng, Y.,Doshi, P.,Chen, Q. (2007). Approximate solutions of interactive dynamic influence diagrams using model clustering. Proceedings of the National Conference on Artificial Intelligence 1 : 782-787. ScholarBank@NUS Repository.|
|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.|
|Source Title:||Proceedings of the National Conference on Artificial Intelligence|
|Appears in Collections:||Staff Publications|
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