Graphical Models for Multiagent Decision Making

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What are Graphical Models for Multiagent Decision Making?
Interactive Dynamic Influence Diagram

An influence diagram (ID) (also called a relevance diagram, decision diagram or a decision network) is a compact graphical and mathematical representation of a decision situation. It is a generalization of a Bayesian network, in which not only probabilistic inference problems but also decision making problems (following maximum expected utility criterion) can be modeled and solved. Their dynamic counterparts, Dynamic Influence Diagrams (DIDs) are graphical representations for partially observable Markov decision processes (POMDPs). Researchers developed new graphical representations for the problem of sequential decision making in partially observable 'multiagent' environments, as formalized by interactive partially observable Markov decision processes (I-POMDPs). The graphical models called interactive influence diagrams (I-IDs) and their dynamic counterparts, interactive dynamic influence diagrams (I-DIDs), seek to explicitly model the structure that is often present in real-world problems by decomposing the situation into chance and decision variables, and the dependencies between the variables. I-DIDs generalize DIDs to multiagent settings in the same way that I-POMDPs generalize POMDPs. I-DIDs may be used to compute the policy of an agent given its belief as the agent acts and observes in a setting that is populated by other interacting agents. These graphical models are significantly more transparent and semantically clear than other previous representations.

Project Description

I-DIDs closely model realistic partially observable multiagent scenarios that are commonly observed in the real world. In such scenarios, the subject agent not only models the uncertainties that may exist in the environment it is in, but also models the uncertainties that may exist in how the other agents behave. While attempting to capture this, algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. We seek to develop efficient exact and approximate methods to efficiently solve them thereby allowing scalability to larger horizons as well as larger problems with more number of states.

Project Generated Resources

Netus v1.0

  • Open-source Interactive dynamic influence diagram (I-DID) source and executable files available for download and use under Affero GNU public license v3.0 ReadMe , Source Code

MAID-Solver v1.0

  • Open-source multiagent influence diagram (MAID) solver available for download and use under Affero GNU public license v3.0 ReadMe , Source Code


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