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Abstract: In this paper we show how influence diagrams (IDs) can be combined for modeling a multiagent environment. Influence diagrams are a compact representation of a joint probability distribution where actions and utilities are explicitly modeled. (Update)
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BibTeX entry: (Update)
@misc{ maes-modeling,
author = "Sam Maes and Karl Tuyls and Bernard Manderick",
title = "Modeling a Multi-Agent Environment Combining Influence Diagrams",
url = "citeseer.ist.psu.edu/529617.html" }
Citations (may not include all citations):1543 Probabilistic Reasoning in Intelligent Systems: Networks of .. (context) - Pearl - 1988
782 Prentice Hall Series in Artificial Intelligence (context) - Russell, Norvig et al. - 1995
614 Reinforcement Learning: An Introduction - Sutton, Barto - 1998
53 From Influence Diagrams to Junction Trees - Jensen, Jensen et al. - 1994
15 Dependency Networks for Inference (context) - Heckerman - 2000
14 Multiagent reinforcement learning in stochastic games - Hu, Wellman - 1999
12 Probabilistic inference in influence diagrams - Zhang - 1998
10 Markov games as a framework for multi-agent reinforcement le.. (context) - Litmann - 1994
8 Learning Automata and Pareto Optimality for Coordination in .. (context) - Nowe, Verbeeck - 2000
8 LAZY propagation: A junction tree inference algorithm based .. (context) - Jensen, Madsen - 1999
2 Submitted at ECOMAS-GECCO (context) - Defaweux, Lenaerts et al. - 2001
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