An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Reasoning with Cause and Effect
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The Frame Problem and Bayesian Network Action Representation
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Probabilistic propositional planning: representations and complexity
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
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Heterogeneous temporal probabilistic agents
ACM Transactions on Computational Logic (TOCL)
A Logic-Based Approach to Finding Explanations for Discrepancies in Optimistic Plan Execution
Fundamenta Informaticae
Aggregates in Generalized Temporally Indeterminate Databases
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
A Logical Framework to Reinforcement Learning Using Hybrid Probabilistic Logic Programs
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Reasoning about actions with sensing under qualitative and probabilistic uncertainty
ACM Transactions on Computational Logic (TOCL)
Probabilistic Reasoning by SAT Solvers
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Encoding probabilistic causal model in probabilistic action language
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Probabilistic Planning with Imperfect Sensing Actions Using Hybrid Probabilistic Logic Programs
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Cp-logic: A language of causal probabilistic events and its relation to logic programming
Theory and Practice of Logic Programming
Annotated probabilistic temporal logic
ACM Transactions on Computational Logic (TOCL)
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Approximate achievability in event databases
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Detecting and repairing anomalous evolutions in noisy environments
Annals of Mathematics and Artificial Intelligence
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Game-theoretic reasoning about actions in nonmonotonic causal theories
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
Probabilistic reasoning about actions in nonmonotonic causal theories
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Structure-based causes and explanations in the independent choice logic
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A Logic-Based Approach to Finding Explanations for Discrepancies in Optimistic Plan Execution
Fundamenta Informaticae
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In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl.The main feature of our language is its use of static and dynamic causal laws, and use of unknown (or background) variables - whose values are determined by factors beyond our model - in incorporating probabilities. We use two kind of unknown variables: inertial and non-inertial. Inertial unknown variables are helpful in assimilating observations and modeling counterfactuals and causality; while non-inertial unknown variables help characterize stochastic behavior, such as the outcome of tossing a coin, that are not impacted by observations. Finally, we give a glimpse of incorporating probabilities into reasoning with narratives.