Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
An analysis of first-order logics of probability
Artificial Intelligence
Explanation in Bayesian belief networks
Explanation in Bayesian belief networks
Artificial Intelligence
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Constructing Flexible Dynamic Belief Networks from First-Order Probalistic Knowledge Bases
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Decision-theoretic foundations for causal reasoning
Journal of Artificial Intelligence Research
Explanation, irrelevance and statistical independence
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Complexity results for explanations in the structural-model approach
Artificial Intelligence
Causes and explanations in the structural-model approach: tractable cases
Artificial Intelligence
International Journal of Hybrid Intelligent Systems - HIS 2007
A general framework for generating multivariate explanations in Bayesian networks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Causes and explanations revisited
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Causes and explanations: a structural-model approach-part II: explanations
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Causes and explanations in the structural-model approach: Tractable cases
Artificial Intelligence
Most Relevant Explanation: properties, algorithms, and evaluations
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Most probable explanations in Bayesian networks: Complexity and tractability
International Journal of Approximate Reasoning
Causes and explanations in the structural-model approach
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Structure-based causes and explanations in the independent choice logic
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Liberalizing protocols for argumentation in multi-agent systems
ArgMAS'05 Proceedings of the Second international conference on Argumentation in Multi-Agent Systems
Abductive inference in bayesian networks: finding a partition of the explanation space
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Most relevant explanation in Bayesian networks
Journal of Artificial Intelligence Research
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As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to explanation in the literature-- one due to Gärdenfors and one due to Pearl--and show that both suffer from significant problems. We propose an approach to defining a notion of "better explanation" that combines some of the features of both together with more recent work by Pearl and others on causality.