Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An algorithm for probabilistic least-commitment planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Control strategies for a stochastic planner
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Planning with deadlines in stochastic domains
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Distributed constraint optimization with structured resource constraints
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Combining Ontology Engineering with HTN Planning
Proceedings of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering
An HTN Planning Approach for Power System Scheduling Using SHOP2
Proceedings of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering
The deterministic part of IPC-4: an overview
Journal of Artificial Intelligence Research
Engineering benchmarks for planning: the domains used in the deterministic part of IPC-4
Journal of Artificial Intelligence Research
Multi-objective optimisation of power restoration in electricity distribution systems
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Considering Equality on Distributed Constraint Optimization Problem for Resource Supply Network
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Planning with MIP for supply restoration in power distribution systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Integrating diagnosis and repair is particularly crucial when gaining sufficient information to discriminate between several candidate diagnoses requires carrying out some repair actions. A typical case is supply restoration in a faulty power distribution system. This problem, which is a major concern for electricity distributors, features partial observability, and stochastic repair actions which axe more elaborate than simple replacement of components. This paper analyses the difficulties in applying existing work on integrating model-based diagnosis and repair and on planning in partially observable stochastic domains to this real-world problem, and describes the pragmatic approach we have retained so far.