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This paper is concerned with a specific type of problem, namely dynamic decisions, for which most techniques fail to provide adequate solutions. Here, we present two of the most promising optimization techniques, partially observable Markov decision processes (POMDP) and dynamic decision networks (DDN), while arguing which is the most suitable for this problem domain. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
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A tutorial on partially observable Markov decision processes 总被引:1,自引:0,他引:1
Michael L. Littman 《Journal of mathematical psychology》2009,53(3):119-125
The partially observable Markov decision process (POMDP) model of environments was first explored in the engineering and operations research communities 40 years ago. More recently, the model has been embraced by researchers in artificial intelligence and machine learning, leading to a flurry of solution algorithms that can identify optimal or near-optimal behavior in many environments represented as POMDPs. The purpose of this article is to introduce the POMDP model to behavioral scientists who may wish to apply the framework to the problem of understanding normative behavior in experimental settings. The article includes concrete examples using a publicly-available POMDP solution package. 相似文献
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This article presents an agent architecture for controlling an autonomous agent in stochastic, noisy environments. The architecture combines the partially observable Markov decision process (POMDP) model with the belief-desire-intention (BDI) framework. The Hybrid POMDP-BDI agent architecture takes the best features from the two approaches, that is, the online generation of reward-maximizing courses of action from POMDP theory, and sophisticated multiple goal management from BDI theory. We introduce the advances made since the introduction of the basic architecture, including (i) the ability to pursue and manage multiple goals simultaneously and (ii) a plan library for storing pre-written plans and for storing recently generated plans for future reuse. A version of the architecture is implemented and is evaluated in a simulated environment. The results of the experiments show that the improved hybrid architecture outperforms the standard POMDP architecture and the previous basic hybrid architecture for both processing speed and effectiveness of the agent in reaching its goals. 相似文献
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