Using simulation and critical points to define states in continuous search spaces

Many artificial intelligence techniques rely on the notion ate" as an abstraction of the actual state of the nd 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 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 n...

• Type: Conference
• Year: 2000
• Where: WSC
• Authors: Marc S. Atkin, Paul R. Cohen
• Keywords: Artificial Intelligence Techniques, Continuous Search Space, Modeling And Simulation, Search Space, WSC 2000