Applications of circumscription to formalizing common-sense knowledge
Artificial Intelligence
On the complexity of propositional knowledge base revision, updates, and counterfactuals
Artificial Intelligence
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Approximate learning of dynamic models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Searching for planning operators with context-dependent and probabilistic effects
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Discovering the hidden structure of complex dynamic systems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning action models from plan examples using weighted MAX-SAT
Artificial Intelligence
Learning Behaviors Models for Robot Execution Control
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Learning partially observable action schemas
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning partially observable action models: efficient algorithms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning planning rules in noisy stochastic worlds
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
Learning HTN method preconditions and action models from partial observations
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
Learning action models for multi-agent planning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Action-model acquisition from noisy plan traces
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present the first tractable, exact solution for the problem of identifying actions' effects in partially observable STRIPS domains. Our algorithms resemble Version Spaces and Logical Filtering, and they identify all the models that are consistent with observations. They apply in other deterministic domains (e.g., with conditional effects), but are inexact (may return false positives) or inefficient (we could not bound the representation size). Our experiments verify the theoretical guarantees, and show that we learn STRIPS actions efficiently, with time that is significantly better than approaches for HMMs and Reinforcement Learning (which are inexact). Our results are especially surprising because of the inherent intractability of the general deterministic case. These results have been applied to an autonomous agent in a virtual world, facilitating decision making, diagnosis, and exploration.