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ECML
2005
Springer
13 years 10 months ago
Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes
Partially Observable Markov Decision Processes (POMDP) provide a standard framework for sequential decision making in stochastic environments. In this setting, an agent takes actio...
Masoumeh T. Izadi, Doina Precup
ATAL
2008
Springer
13 years 6 months ago
Supervision and diagnosis of joint actions in multi-agent plans
The paper formalizes a distributed approach to the problem of supervising the execution of a multi-agent plan where (possibly joint) actions are executed concurrently by a team of...
Roberto Micalizio, Pietro Torasso
AIPS
2006
13 years 6 months ago
Goal Achievement in Partially Known, Partially Observable Domains
We present a decision making algorithm for agents that act in partially observable domains which they do not know fully. Making intelligent choices in such domains is very difficu...
Allen Chang, Eyal Amir
ECAI
2010
Springer
13 years 6 months ago
Learning action effects in partially observable domains
We investigate the problem of learning action effects in partially observable STRIPS planning domains. Our approach is based on a voted kernel perceptron learning model, where act...
Kira Mourão, Ronald P. A. Petrick, Mark Ste...
IJCAI
2003
13 years 6 months ago
A Planning Algorithm for Predictive State Representations
We address the problem of optimally controlling stochastic environments that are partially observable. The standard method for tackling such problems is to define and solve a Part...
Masoumeh T. Izadi, Doina Precup