Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Conditional nonlinear planning
Proceedings of the first international conference on Artificial intelligence planning systems
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Common KADS Library for Expertise Modelling
Common KADS Library for Expertise Modelling
Contingency Selection in Plan Generation
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Planning for contingencies: a decision-based approach
Journal of Artificial Intelligence Research
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
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In classical planning, actions are assumed to be deterministic, the initial state is known and the goal is defined by a set of state facts, and the solution consists of a sequence of actions that leads the system from the initial state to the goal state. However, most practical problems, especially in non observable and uncertain contexts, do not satisfy these requirements of complete and deterministic information. The main goal of this work is to develop a generic planning under uncertainty model at the knowledge level enabling plan viability evaluation so that the most possible, effective, and complete plan can be determined. The proposed model in this work is presented at different levels of analysis: meta ontological, ontological, epistemological and logical levels, and applied to the post and ex ante approaches. The planning task is composed of a set of planning subtasks: plan generation, plan prevention, plan support, plan correction, and plan replacement.