Regression and progression in stochastic domains
DOI10.1016/j.artint.2020.103247zbMath1435.68311OpenAlexW3003472872MaRDI QIDQ2303514
Hector J. Levesque, Vaishak Belle
Publication date: 4 March 2020
Published in: Artificial Intelligence (Search for Journal in Brave)
Full work available at URL: https://www.pure.ed.ac.uk/ws/files/132362762/Regression_and_Progression_BELLE_DOA25012020_AFV.pdf
knowledge representationcognitive roboticsreasoning about actionreasoning about knowledgereasoning about uncertainty
Logic in artificial intelligence (68T27) Knowledge representation (68T30) Reasoning under uncertainty in the context of artificial intelligence (68T37) Artificial intelligence for robotics (68T40)
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- Planning and acting in partially observable stochastic domains
- A logic-based calculus of events
- An analysis of first-order logics of probability
- Knowledge, action, and the frame problem
- Dynamic update with probabilities
- Extending probabilistic dynamic epistemic logic
- Probabilistic logic
- How to progress a database
- Probabilistic dynamic epistemic logic
- Some first-order probability logics
- Reasoning about noisy sensors and effectors in the situation calculus
- Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems
- Reasoning robots. The art and science of programming robotic agents
- From Situation Calculus to Dynamic Epistemic Logic
- Probabilistic Databases
- Dynamic Epistemic Logic and Knowledge Puzzles
- A Semantical Account of Progression in the Presence of Defaults
- Representing action and change by logic programs
- Reasoning about knowledge and probability
- GOLOG: A logic programming language for dynamic domains
- Logic and Probabilistic Update
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