Planning for conjunctive goals
Artificial Intelligence
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Artificial Intelligence
Depth-limited search for real-time problem solving
Real-Time Systems
The entropy reduction engine: integrating planning, scheduling, and control
ACM SIGART Bulletin
Planning under time constraints in stochastic domains
Artificial Intelligence - Special volume on planning and scheduling
Plan reuse versus plan generation: a theoretical and empirical analysis
Artificial Intelligence - Special volume on planning and scheduling
Fast planning through planning graph analysis
Artificial Intelligence
Intelligent planning: a decomposition and abstraction based approach
Intelligent planning: a decomposition and abstraction based approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
AI Magazine
Anytime Planning for Optimal Tradeoff between Deliberative and Reactive Planning
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
S-MEP: A Planner for Numeric Goals
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Queue - Game Development
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Reactive planning simulation in dynamic environments with VirtualRobot
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Artificial Intelligence and Grids: Workflow Planning and Beyond
IEEE Intelligent Systems
Interleaving temporal planning and execution in robotics domains
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
Planning for contingencies: a decision-based approach
Journal of Artificial Intelligence Research
The GRT planning system: backward heuristic construction in forward state-space planning
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Deploying information agents on the web
IJCAI'03 Proceedings of the 18th international joint conference on 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
Dynamic planning approach to automated web service composition
Applied Intelligence
A universal planning system for hybrid domains
Applied Intelligence
A resource enhanced HTN planning approach for emergency decision-making
Applied Intelligence
Reasoning about shadows in a mobile robot environment
Applied Intelligence
A knowledge-based architecture for the management of patient-focused care pathways
Applied Intelligence
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In this paper, we present a novel and domain-independent planner aimed at working in highly dynamic environments with time constraints. The planner follows the anytime principles: a first solution can be quickly computed and the quality of the final plan is improved as long as time is available. This way, the planner can provide either fast reactions or very good quality plans depending on the demands of the environment. As an on-line planner, it also offers important advantages: our planner allows the plan to start its execution before it is totally generated, unexpected events are efficiently tackled during execution, and sensing actions allow the acquisition of required information in partially observable domains. The planning algorithm is based on problem decomposition and relaxation techniques. The traditional relaxed planning graph has been adapted to this on-line framework by considering information about sensing actions and action costs. Results also show that our planner is competitive with other top-performing classical planners.