Learning Constraints via Demonstration for Safe Planning

Ugur Kuter, Geoffrey Levine, Derek Green, Anton Rebguns, Diana Spears, Gerald DeJong

A key challenge of automated planning, including ``safe planning,'' is the requirement of a domain expert to provide the background knowledge, including some set of safety constraints. To alleviate the infeasibility of acquiring complete and correct knowledge from human experts in many complex, real-world domains, this paper investigates a technique for automated extraction of safety constraints by observing a user demonstration trace. In particular, we describe a new framework based on maximum likelihood learning for generating constraints on the concepts and properties in a domain ontology for a planning domain. Then, we describe a generalization of this framework that involves Bayesian learning of such constraints. To illustrate the advantages of our framework, we provide and discuss examples on a real test application for Airspace Control Order (ACO) planning, a benchmark application in the DARPA Integrated Learning Program.

Subjects: 10. Knowledge Acquisition; 12. Machine Learning and Discovery

Submitted: May 15, 2007


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