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Simplifying computation of dynamic influence diagrams

Hongliang Yao, Hao Wang, Yousheng Zhang, Xuegang Hu, Baofu Fang

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Abstract

Influence diagrams (EDs) are based on Bayesian networks (BNs) and decision theory, and they are powerful tools for representing and processing problems of agents. An approach of decomposition and incorporation is developed to solve problems of multi-agent system based on influence diagrams and dynamic Bayesian networks (DBNs) that is intractable for exact calculation. In additional, we discuss realizing decision of dynamic influence diagrams (DIDs) and reducing computation of decision problems.

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What this paper is about

Influence diagrams (EDs) are based on Bayesian networks (BNs) and decision theory, and they are powerful tools for representing and processing problems of agents. An approach of decomposition and incorporation is developed to solve problems of multi-agent system based on influence diagrams and dynamic Bayesian networks (DBNs) that is intractable for exact calculation. In additional, we discuss realizing decision of dynamic influence diagrams (DIDs) and reducing computation of decision problems.

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Available abstract

Influence diagrams (EDs) are based on Bayesian networks (BNs) and decision theory, and they are powerful tools for representing and processing problems of agents. An approach of decomposition and incorporation is developed to solve problems of multi-agent system based on influence diagrams and dynamic Bayesian networks (DBNs) that is intractable for exact calculation. In additional, we discuss realizing decision of dynamic influence diagrams (DIDs) and reducing computation of decision problems.

Key concepts: Influence diagram, Computer science, Dynamic Bayesian network, Bayesian network, Computation, Decomposition, Theoretical computer science, Bayesian probability

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