Reasoning about action II: the qualification problem
Artificial Intelligence
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
On the logic of iterated belief revision
Artificial Intelligence
Solving the frame problem: a mathematical investigation of the common sense law of inertia
Solving the frame problem: a mathematical investigation of the common sense law of inertia
Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
Formalizing sensing actions—a transition function based approach
Artificial Intelligence
The Qualification Problem: A solution to the problem of anomalous models
Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Knowledge, action, and the frame problem
Artificial Intelligence
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
Semantical consideration on floyo-hoare logic
SFCS '76 Proceedings of the 17th Annual Symposium on Foundations of Computer Science
Epistemological problems of artificial intelligence
IJCAI'77 Proceedings of the 5th 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
The independent choice logic and beyond
Probabilistic inductive logic programming
Iterated belief change in the situation calculus
Artificial Intelligence
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Nondeterminism is pervasive in all but the simplest action domains: an agent may flip a coin or pick up a different object than intended, or an action may fail and may fail in different ways. In this paper we provide a qualitative theory of nondeterminism. The account is based on an epistemic extension to the situation calculus that accommodates sensing actions. Our position is that nondeterminism is an epistemic phenomenon, and that the world is most usefully regarded as deterministic. Nondeterminism arises from an agent's limited awareness and perception. The account offers several advantages: an agent has a set of categorical (as opposed to probabilistic) beliefs, yet can deal with equally-likely outcomes (such as in flipping a fair coin) or with outcomes of differing plausibility (such as an action that may on rare occasion fail).