Artificial Intelligence - Special volume on qualitative reasoning about physical systems
The complexity of robot motion planning
The complexity of robot motion planning
Optimal path-finding algorithms*
Search in Artificial Intelligence
Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
Explaining and repairing plans that fail
Artificial Intelligence
Planning and control
Temporal planning with continuous change
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Robot Motion Planning
Hierarchical agent control: a framework for defining agent behavior
Proceedings of the fifth international conference on Autonomous agents
Simulation using software agents II: domain-general simulation and planning with physical schemas
Proceedings of the 32nd conference on Winter simulation
HAC: A Unified View of Reactive and Deliberative Activity
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
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Many artificial intelligence techniques rely on the notion of a "state" as an abstraction of the actual state of the world, and an "operator" as an abstraction of the actions that take you from one state to the next. Much of the art of problem solving depends on choosing the appropriate set of states and operators. However, in realistic, and therefore dynamic and continuous search spaces, finding the right level of abstraction can be difficult. If too many states are chosen, the search space becomes intractable; if too few are chosen, important interactions between operators might be missed, making the search results meaningless. We present the idea of simulating operators using critical points as a way of dynamically defining state boundaries; new states are generated as part of the process of applying operators. Critical point simulation allows the use of standard search and planning techniques in continuous domains, as well as the incorporation of multiple agents, dynamic environments, and non-atomic variable length actions into the search algorithm. We conclude with examples of implemented systems that show how critical points are used in practice.