Artificial Intelligence - Special issue on knowledge representation
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The New Science of Management Decision
The New Science of Management Decision
Cognitive Support for Real-Time Dynamic Decision Making
Information Systems Research
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Applications of SHOP and SHOP2
IEEE Intelligent Systems
IEEE Intelligent Systems
Plan management in the medical domain
AI Communications
Coordination in emergency response management
Communications of the ACM - Web searching in a multilingual world
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
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Intelligent Decision Making: An AI-Based Approach
Intelligent Decision Making: An AI-Based Approach
Planning with resources and concurrency a forward chaining approach
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Temporal enhancements of an HTN planner
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
A Cognitive Model of Improvisation in Emergency Management
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Extreme events challenge emergency managers' decision making capabilities of developing unified action plans to ensure effective coordination for multiple responding agencies. In this paper, we present a novel HTN planner, called XEPlanner, aimed at working in dynamic emergency response environment with critical time constraints. By understanding incident action plans developing process and characteristics of flood controlling, this paper proposes a set of planning requirements as the guidance for designing a domain specific HTN planner, which challenge the current known HTN planners. Based on the classical AI planning technologies, our planner is designed and implemented for adapting to the application domain. It follows the anytime principles: the first feasible plan can be quickly produced and the quality of the plans is improved as more time is available. Additionally, a set of heuristics for selecting search nodes is proposed for taking into account preferences of emergency managers during the planning process. Moreover, the priorities of incident objectives can be handled effectively. Our experimental results demonstrate that our planner satisfies all the proposed planning requirements and has advantages when it's applied in flood evacuation domain.