Introduction to artificial intelligence
Introduction to artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
Towards Combining Inductive Logic Programming with Bayesian Networks
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Learning probabilistic logic models from probabilistic examples
Machine Learning
Probabilistic Explanation Based Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Journal of Artificial Intelligence Research
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
On the mechanization of abductive logic
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
A general model for online probabilistic plan recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning structure and parameters of stochastic logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Basic principles of learning Bayesian logic programs
Probabilistic inductive logic programming
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
On the role of coherence in abductive explanation
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A probabilistic model of plan recognition
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Implementing weighted abduction in Markov logic
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Generating artificial corpora for plan recognition
UM'05 Proceedings of the 10th international conference on User Modeling
Variational bayes inference for logic-based probabilistic models on BDDs
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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Plan recognition is the task of predicting an agent's top-level plans based on its observed actions. It is an abductive reasoning task that involves inferring cause from effect. Most existing approaches to plan recognition use either first-order logic or probabilistic graphical models. While the former cannot handle uncertainty, the latter cannot handle structured representations. In order to overcome these limitations, we develop an approach to plan recognition using Bayesian Logic Programs (BLPs), which combine first-order logic and Bayesian networks. Since BLPs employ logical deduction to construct the networks, they cannot be used effectively for plan recognition. Therefore, we extend BLPs to use logical abduction to construct Bayesian networks and call the resulting model Bayesian Abductive Logic Programs (BALPs). We learn the parameters in BALPs using the Expectation Maximization algorithm adapted for BLPs. Finally, we present an experimental evaluation of BALPs on three benchmark data sets and compare its performance with the state-of-the-art for plan recognition.