Planning under time constraints in stochastic domains
Artificial Intelligence - Special volume on planning and scheduling
Fast planning through planning graph analysis
Artificial Intelligence
Casper: Space Exploration through Continuous Planning
IEEE Intelligent Systems
Anytime Planning for Optimal Tradeoff between Deliberative and Reactive Planning
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Contingent planning under uncertainty via stochastic satisfiability
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Performance bounds for planning in unknown terrain
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
Planning for contingencies: a decision-based approach
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
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In this paper we present a novel planning approach, based on well-known techniques such as goal decomposition and heuristic planning, aimed at working in highly dynamic environments with time constraints. Our contribution is a domain-independent planner to incrementally generate plans under a deliberative framework for reactive domains. The planner follows the anytime principles, i.e a first solution plan can be quickly computed and the quality of the solution is improved as time is available. Moreover, the fast computation of the sequential actions allows the plan to start its execution before it is totally generated, thus giving rise to a highly reactive planning system.