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» Observational learning in an uncertain world
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IJAR
2008
161views more  IJAR 2008»
13 years 7 months ago
Bayesian learning for a class of priors with prescribed marginals
We present Bayesian updating of an imprecise probability measure, represented by a class of precise multidimensional probability measures. Choice and analysis of our class are mot...
Hermann Held, Thomas Augustin, Elmar Kriegler
AI
2007
Springer
13 years 7 months ago
Learning action models from plan examples using weighted MAX-SAT
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...
Qiang Yang, Kangheng Wu, Yunfei Jiang
AAAI
2000
13 years 8 months ago
A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments
If robotic agents are to act autonomously they must have the ability to construct and reason about models of their physical environment. For example, planning to achieve goals req...
Tim Oates, Matthew D. Schmill, Paul R. Cohen
ICML
2004
IEEE
14 years 8 months ago
Utile distinction hidden Markov models
This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Part...
Daan Wierstra, Marco Wiering
LOCA
2007
Springer
14 years 1 months ago
Inferring the Everyday Task Capabilities of Locations
Abstract. People rapidly learn the capabilities of a new location, without observing every service and product. Instead they map a few observations to familiar clusters of capabili...
Patricia Shanahan, William G. Griswold