On the complexity of unique solutions
Journal of the ACM (JACM)
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Complexity results for structure-based causality
Artificial Intelligence
Causes and Explanations: A Structural-Model Approach: Part 1: Causes
UAI '01 Proceedings of the 17th Conference in Uncertainty in 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
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Social Judgment in Multiagent Interactions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Predicting causality ascriptions from background knowledge: model and experimental validation
International Journal of Approximate Reasoning
Social Responsibility among deliberative agents
Proceedings of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium
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Causality is typically treated an all-or-nothing concept; either A is a cause of B or it is not. We extend the definition of causality introduced by Halpern and Pearl 2001a to take into account the degree of responsibility of A for B. For example, if someone wins an election 11-0, then each person who votes for him is less responsible for the victory than if he had won 6-5. We then define a notion of degree of blame, which takes into account an agent's epistemic state. Roughly speaking, the degree of blame of A for D is the expected degree of responsibility of A for B, taken over the epistemic state of an agent.