250 search results - page 2 / 50 » Learning action effects in partially observable domains |
233
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AAAI
2012
13 years 9 months ago
2012
We present the first real-world benchmark for sequentiallyoptimal team formation, working within the framework of a class of online football prediction games known as Fantasy Foo...
203
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ECML
2005
Springer
16 years 3 days ago
2005
Springer
Partially Observable Markov Decision Processes (POMDP) provide a standard framework for sequential decision making in stochastic environments. In this setting, an agent takes actio...
185
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AI
2007
Springer
15 years 6 months ago
2007
Springer
AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as inp...
183
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ICML
1999
IEEE
16 years 7 months ago
1999
IEEE
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
172
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AAAI
2010
15 years 8 months ago
2010
Relational Markov Decision Processes (MDP) are a useraction for stochastic planning problems since one can develop abstract solutions for them that are independent of domain size ...