Artificial Intelligence
A logic of universal causation
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Encoding Solutions of the Frame Problem in Dynamic Logic
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Causes and Explanations: A Structural-Model Approach: Part 1: Causes
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
EPDL: a logic for causal reasoning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A causal theory of ramifications and qualifications
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Embracing causality in specifying the indirect effects of actions
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Causal theories of action and change
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Conditional logic of actions and causation
Artificial Intelligence - Special issue on nonmonotonic reasoning
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In this paper we present a new approach to reason about actions and causation which is based on a conditional logic. The conditional implication is interpreted as causal implication. This makes it possible to formalize in a uniform way causal dependencies between actions and their immediate and indirect effects. Also, it provides a natural formalization of concurrent actions and causal dependencies between actions. An abductive semantics is adopted for dealing with the frame problem.