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NIPS
2004
13 years 8 months ago
Schema Learning: Experience-Based Construction of Predictive Action Models
Schema learning is a way to discover probabilistic, constructivist, predictive action models (schemas) from experience. It includes methods for finding and using hidden state to m...
Michael P. Holmes, Charles Lee Isbell Jr.
ECP
1997
Springer
130views Robotics» more  ECP 1997»
13 years 11 months ago
Encoding Planning Problems in Nonmonotonic Logic Programs
We present a framework for encoding planning problems in logic programs with negation as failure, having computational e ciency as our major consideration. In order to accomplish o...
Yannis Dimopoulos, Bernhard Nebel, Jana Koehler
AIPS
2007
13 years 9 months ago
Discovering Relational Domain Features for Probabilistic Planning
In sequential decision-making problems formulated as Markov decision processes, state-value function approximation using domain features is a critical technique for scaling up the...
Jia-Hong Wu, Robert Givan
ICMI
2005
Springer
136views Biometrics» more  ICMI 2005»
14 years 1 months ago
Probabilistic grounding of situated speech using plan recognition and reference resolution
Situated, spontaneous speech may be ambiguous along acoustic, lexical, grammatical and semantic dimensions. To understand such a seemingly difficult signal, we propose to model th...
Peter Gorniak, Deb Roy
AAAI
2010
13 years 9 months ago
Automatic Derivation of Finite-State Machines for Behavior Control
Finite-state controllers represent an effective action selection mechanisms widely used in domains such as video-games and mobile robotics. In contrast to the policies obtained fr...
Blai Bonet, Héctor Palacios, Hector Geffner