Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
Hard problems for simple default logics
Proceedings of the first international conference on Principles of knowledge representation and reasoning
What does a conditional knowledge base entail?
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Causal default reasoning: principles and algorithms
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Unfounded sets and well-founded semantics for general logic programs
Proceedings of the seventh ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Default Reasoning: Causal and Conditional Theories
Default Reasoning: Causal and Conditional Theories
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We analyze conditions that allow for sound and efficient non-monotonic inference For that we consider theories comprised of rules and observations and a semantic framework developed elsewhere that allows us to view such theories as dynamic systems systems with a transition function f that maps states to sets of possible successor states and a plausibility function that determines the relative likelihood of those transitions In this framework the transition function f is determined by the rules and the plausibility function is provided independently In this work we aim to identify plausibility functions that have good semantical and computational properties We do so by identifying a vet of tore predictions to be accounted for that can be computed in polynomial time, can be justified in simple terms and are not tied to either Horn theories or closed world assumptions. The resulting functions allow us to handle an interesting class of theories in a justifiable and efficient manner.