Please use this identifier to cite or link to this item:
|Title:||Graphical models for interactive POMDPs: Representations and solutions|
Probabilistic graphical models
Sequential multiagent decision making
|Citation:||Doshi, P., Zeng, Y., Chen, Q. (2009). Graphical models for interactive POMDPs: Representations and solutions. Autonomous Agents and Multi-Agent Systems 18 (3) : 376-416. ScholarBank@NUS Repository. https://doi.org/10.1007/s10458-008-9064-7|
|Abstract:||We develop 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, which may be viewed as graphical representations of POMDPs, 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. Using several examples, we show how I-IDs and I-DIDs may be applied and demonstrate their usefulness. We also show how the models may be solved using the standard algorithms that are applicable to DIDs. Solving I-DIDs exactly involves knowing the solutions of possible models of the other agents. The space of models grows 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 models that are likely to be behaviorally equivalent and selecting a representative set from the clusters. We discuss the error bound of the approximation technique and demonstrate its empirical performance. © 2008 Springer Science+Business Media, LLC.|
|Source Title:||Autonomous Agents and Multi-Agent Systems|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 19, 2019
WEB OF SCIENCETM
checked on Feb 11, 2019
checked on Oct 13, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.