Learning constraints via demonstration for safe planning

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationAcquiring Planning Knowledge via Demonstration - Papers from the 2007 AAAI Workshop, Technical Report
Number of pages6
StatePublished - 2007
Externally publishedYes
Event2007 AAAI Workshop - Vancouver, BC, Canada
Duration: Jul 23 2007Jul 23 2007

Publication series

NameAAAI Workshop - Technical Report


Other2007 AAAI Workshop
CityVancouver, BC

ASJC Scopus subject areas

  • Engineering(all)


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