Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Artificial intelligence and mathematical theory of computation
Artificial Intelligence
Deriving invariants and constraints from action theories
Fundamenta Informaticae
On the logic of causal explanation
Artificial Intelligence
Inferring state constraints for domain-independent planning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
On strongest neccessary and weakest sufficient conditions
Artificial Intelligence
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
Computing ramifications by postprocessing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Reasoning about actions: non-deterministic effects, constraints, and qualification
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
Embracing causality in specifying the indeterminate effects of actions
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Loop formulas for circumscription
Artificial Intelligence
Recycling computed answers in rewrite systems for abduction
ACM Transactions on Computational Logic (TOCL)
Loop formulas for circumscription
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Loop formulas for circumscription
Artificial Intelligence
Journal of Artificial Intelligence Research
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We describe a system for specifying the effects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the effects of actions using domain rules, which are state constraints that can entail new action effects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of logical theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action specifications for classical AI planning systems, GOLOG systems, and other systems where formal specifications of actions are needed.