The IJCAI-09 Workshop on Learning Structural Knowledge From Observations (STRUCK-09)

Kuter, Ugur (University of Maryland) | Munoz-Avila, Hector (Lehigh University)

AI Magazine 

These formalisms have in common the use of certain kinds of constructs (for example, objects, goals, skills, and tasks) that represent knowledge of varying degrees of complexity and that are connected through structural relations. In recent years, we have observed increasing interest toward the problem of learning such structural knowledge from observations. These observations range from traces generated by an automated planner to video feeds from a robot performing some actions. The goal of the workshop was to bring researchers together from machine learning, automated planning, case-based reasoning, cognitive science, and other communities that are looking into instances of this problem and to share ideas and perspectives in a common forum.

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