A logic-based calculus of events
New Generation Computing
Features and fluents (vol. 1): the representation of knowledge about dynamical systems
Features and fluents (vol. 1): the representation of knowledge about dynamical systems
An action language based on causal explanation: preliminary report
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Temporal Prediction: Dealing with Change and Interactions within a Causal Framework
AI*IA '95 Proceedings of the 4th Congress of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence
An Efficient Algorithm for Temporal Abduction
AI*IA '97 Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Planning with concurrent interacting actions
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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We propose to extend the temporal causal graph formalisms used in model-based diagnosis in order to deal with non trivial interactions like (partial) cancellation of fault effects. A high-level causal language is defined in which properties such as the persistence of effects and the triggering or sustaining properties of causes can be expressed. Various interaction phenomena are associated with these features. Instead of proposing an ad hoc reasoning mechanism to process this extended formalism, the specifications in this language are automatically translated into an event calculus based language having well-established semantics. Our approach improves the way fault interaction and intermittent faults are coped with in temporal abductive diagnosis.