Learning Action Models by Observing Other Agents

Xuemei Wang

Autonomous agents should have the ability to learn from the environment. The actions of other agents in the environment provide a useful source for such learning[Dent, 1990]. This paper presents a framework for learning action models by observing other agents in the context of PRODIGY [Carbonell et al. , 1990], a general-purpose planner. A domain in PRODIGY is specified by a set of operators that represent actions. The system learns the operators incrementally through a close-loop integration of observing other agents, learning, planning, and executing plans in the environment,

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