Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Foundations of logic programming; (2nd extended ed.)
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Probabilistic reasoning in intelligent systems: networks of plausible inference
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Explanation and prediction: an architecture for default and abductive reasoning
Computational Intelligence
A model for reasoning about persistence and causation
Computational Intelligence
Logic and artificial intelligence
Artificial Intelligence
Planning and control
Decision analysis and expert systems
AI Magazine
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Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
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Acting optimally in partially observable stochastic domains
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The sciences of the artificial (3rd ed.)
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An optimal approximation algorithm for Bayesian inference
Artificial Intelligence
The independent choice logic for modelling multiple agents under uncertainty
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Solving the frame problem: a mathematical investigation of the common sense law of inertia
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Computational intelligence: a logical approach
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Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
Causality: models, reasoning, and inference
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Dynamic Programming and Optimal Control
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Introduction to Bayesian Networks
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Symbolic Logic and Mechanical Theorem Proving
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Introduction to Reinforcement Learning
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Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
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Reasoning with Cause and Effect
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An Introduction to Variational Methods for Graphical Models
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Probabilistic partial evaluation: exploiting rule structure in probabilistic inference
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Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Operations for learning with graphical models
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Provably bounded-optimal agents
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Prediction is deduction but explanation is abduction
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Input generalization in delayed reinforcement learning: an algorithm and performance comparisons
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Representing diagnostic knowledge for probabilistic Horn abduction
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Exploiting structure in policy construction
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Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Detecting Inference Channels in Private Multimedia Data via Social Networks
Proceedings of the 23rd Annual IFIP WG 11.3 Working Conference on Data and Applications Security XXIII
Graphical readings of possibilistic logic bases
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Privacy preserving via tree augmented naïve Bayesian classifier in multimedia database
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Structure-based causes and explanations in the independent choice logic
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Combining bayesian networks with higher-order data representations
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Discovering relationship types between users using profiles and shared photos in a social network
Multimedia Tools and Applications
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In this paper I give a brief overview of recent work on uncertainty in AI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks for reasoning that emphasize different aspects of intelligent reasoning. Belief networks (Bayesian networks) are representations of independence that form the basis for understanding much of the recent work on reasoning under uncertainty, evidential and causal reasoning, decision analysis, dynamical systems, optimal control, reinforcement learning and Bayesian learning. The independent choice logic provides a bridge between logical representations and belief networks that lets us understand these other representations and their relationship to logic and shows how they can extended to first-order rule-based representations. This paper discusses what the representations of uncertainty can bring to the computational logic community and what the computational logic community can bring to those studying reasoning under uncertainty.