Monte Carlo Sampling Methods for Approximating Interactive POMDPs
نویسندگان
چکیده
منابع مشابه
Monte Carlo Sampling Methods for Approximating Interactive POMDPs
Partially observable Markov decision processes (POMDPs) provide a principled framework for sequential planning in uncertain single agent settings. An extension of POMDPs to multiagent settings, called interactive POMDPs (I-POMDPs), replaces POMDP belief spaces with interactive hierarchical belief systems which represent an agent’s belief about the physical world, about beliefs of other agents, ...
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To act optimally in a partially observable, stochastic and multi-agent environment, an autonomous agent needs to maintain a belief of the world at any given time. An extension of partially observable Markov decision processes (POMDPs), called interactive POMDPs (I-POMDPs), provides a principled framework for planning and acting in such settings. I-POMDP augments the POMDP beliefs by including m...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2009
ISSN: 1076-9757
DOI: 10.1613/jair.2630