Conditional nonlinear planning
Proceedings of the first international conference on Artificial intelligence planning systems
Automatically generating abstractions for planning
Artificial Intelligence
World modeling for the dynamic construction of real-time control plans
Artificial Intelligence
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
Imposing real-time constraints on self-adaptive controller synthesis
IWSAS' 2000 Proceedings of the first international workshop on Self-adaptive software
Self-Adaptive Software for Hard Real-Time Environments
IEEE Intelligent Systems
Exploiting Implicit Representations in Timed Automaton Verification for Controller Synthesis
HSCC '02 Proceedings of the 5th International Workshop on Hybrid Systems: Computation and Control
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Strong planning under partial observability
Artificial Intelligence
Strong planning under partial observability
Artificial Intelligence
Planning under uncertainty and its applications
Reasoning, Action and Interaction in AI Theories and Systems
Self-control of the time complexity of a constraint satisfaction problem solver program
Journal of Systems and Software
Proximity-based non-uniform abstractions for approximate planning
Journal of Artificial Intelligence Research
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This paper describes Dynamic Abstraction Planning (DAP), an abstraction planning technique that improves the efficiency of state-enumeration planners for real-time embedded systems such as CIRCA. Abstraction is used to remove detail from the state representation, reducing both the size of the state space that must be explored to produce a plan and the size of the resulting plan. The intuition behind this approach is simple: in some situations, certain world features are important, while in other situations those same features are not important. By automatically selecting the appropriate level of abstraction at each step during the planning process, DAP can significantly reduce the size of the search space. Furthermore, the planning process can supply initial plans that preserve safety but might, on further refinement, do a better job of goal achievement. DAP can also terminate with an executable abstract plan, which may be much smaller than the corresponding plan expanded to precisely-defined states. Preliminary results show dramatic improvements in planning speed and scalability.