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» The Learning Power of Belief Revision
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UAI
1998
13 years 8 months ago
Learning From What You Don't Observe
The process of diagnosis involves learning about the state of a system from various observations of symptoms or findings about the system. Sophisticated Bayesian (and other) algor...
Mark A. Peot, Ross D. Shachter
JOLLI
2008
145views more  JOLLI 2008»
13 years 7 months ago
Temporal Languages for Epistemic Programs
This paper adds temporal logic to public announcement logic (PAL) and dynamic epistemic logic (DEL). By adding a previous-time operator to PAL, we express in the language statemen...
Joshua Sack
CI
2005
106views more  CI 2005»
13 years 7 months ago
Incremental Learning of Procedural Planning Knowledge in Challenging Environments
Autonomous agents that learn about their environment can be divided into two broad classes. One class of existing learners, reinforcement learners, typically employ weak learning ...
Douglas J. Pearson, John E. Laird
UMUAI
1998
157views more  UMUAI 1998»
13 years 7 months ago
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
We present an approach to keyhole plan recognition which uses a dynamic belief (Bayesian) network to represent features of the domain that are needed to identify users’ plans and...
David W. Albrecht, Ingrid Zukerman, Ann E. Nichols...
HUC
2010
Springer
13 years 7 months ago
Bayesian recognition of motion related activities with inertial sensors
This work presents the design and evaluation of an activity recognition system for seven important motion related activities. The only sensor used is an Inertial Measurement Unit ...
Korbinian Frank, Maria Josefa Vera Nadales, Patric...