Artificial intelligence and mathematical theory of computation
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An integrated shell and methodology for rapid development of knowledge-based agents
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 FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Searching for planning operators with context-dependent and probabilistic effects
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning, planning, and the life cycle of workflow management
EDOC '05 Proceedings of the Ninth IEEE International EDOC Enterprise Computing Conference
Learning action models from plan examples using weighted MAX-SAT
Artificial Intelligence
ARMS: an automatic knowledge engineering tool for learning action models for AI planning
The Knowledge Engineering Review
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Managing the life cycle of plans
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE-IMPOVERISHED SYSTEM
Computational Intelligence
Refining incomplete planning domain models through plan traces
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
This paper presents a framework that justifies an agent's goal-directed behavior, even in the absence of a provably correct plan. Most prior planning systems rely on a complete causal model and circumvent the frame problem by implicitly assuming that no unspecified relationships exist between actions and the world. In our approach, a domain modeler provides explicit statements about which actions have been incompletely specified. Thus, an agent can minimize its dependence on implicit assumptions when selecting an action sequence to achieve its goals.