Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
The Soar papers (vol. 1): research on integrated intelligence
The Soar papers (vol. 1): research on integrated intelligence
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An algorithm for probabilistic planning
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Plan reuse versus plan generation: a theoretical and empirical analysis
Artificial Intelligence - Special volume on planning and scheduling
Computational complexity of planning and approximate planning in the presence of incompleteness
Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Interleaving Planning and Robot Execution for Asynchronous User Requests
Autonomous Robots - Special issue on autonomous agents
SimPlanner: An Execution-Monitoring System for Replanning in Dynamic Worlds
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Polynomial-Length Planning Spans the Polynomial Hierarchy
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Planning as Satisfiability in Nondeterministic Domains
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Mobile Agents and Logic Programming
MA '02 Proceedings of the 6th International Conference on Mobile Agents
A logic programming approach to knowledge-state planning, II: the DLVk system
Artificial Intelligence
Reasoning about actions in a probabilistic setting
Eighteenth national conference on Artificial intelligence
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
A logic programming approach to knowledge-state planning: Semantics and complexity
ACM Transactions on Computational Logic (TOCL)
Diagnostic reasoning with A-Prolog
Theory and Practice of Logic Programming
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
FLUX: A logic programming method for reasoning agents
Theory and Practice of Logic Programming
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
On reversing actions: algorithms and complexity
IJCAI'07 Proceedings of the 20th international joint conference on Artifical 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
A new HTN planning framework for agents in dynamic environments
CLIMA IV'04 Proceedings of the 4th international conference on Computational Logic in Multi-Agent Systems
KMONITOR: a tool for monitoring plan execution in action 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
Intelligent Execution Monitoring in Dynamic Environments
Fundamenta Informaticae - The 1st International Workshop on Knowledge Representation and Approximate Reasoning (KR&AR)
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Consider an agent executing a plan with nondeterministic actions, in a dynamic environment, which might fail. Suppose that she is given a description of this action domain, including specifications of effects of actions, and a set of trajectories for the execution of this plan, where each trajectory specifies a possible execution of the plan in this domain. After executing some part of the plan, suppose that she obtains information about the current state of the world, and notices that she is not at a correct state relative to the given trajectories. How can she find an explanation (a point of failure) for such a discrepancy? An answer to this question can be useful for different purposes. In the context of execution monitoring, points of failure can determine some checkpoints that specify when to check for discrepancies, and they can sometimes be used for recovering from discrepancies that cause plan failures. At the modeling level, points of failure may provide useful insight into the action domain for a better understanding of the domain, or reveal errors in the formalization of the domain. We study the question above in a general logic-based knowledge representation framework, which can accommodate nondeterminism and concurrency. In this framework, we define a discrepancy and an explanation for it, and analyze the computational complexity of detecting discrepancies and finding explanations for them. We introduce a method for computing explanations, and report about a realization of this method using DLV$^K$, which is a logic-programming based system for reasoning about actions and change.