Year: 2009
Type: report
Abstract: We present a Bayesian approach to learning flexible safety constraints and subsequently verifying whether plans satisfy these constraints. Our approach, called the Safety Constraint Learner/Checker (SCLC), is embedded within the Generalized Integrated Learning Architecture (GILA), which is an integrated, heterogeneous, multi-agent ensemble architecture designed for learning complex problem solving techniques from demonstration by human experts. The SCLC infers safety constraints from a single expert demonstration trace, and applies these constraints to the solutions proposed by the agents in the ensemble. Blame for constraint violations is then transmitted to the individual learning/planning/reasoning agents, thereby facilitating new problem-solving episodes. We discuss the advantages of the SCLC and demonstrate empirical results on an Airspace Planning and Deconfliction Task, which was a benchmark application in the DARPA Integrated Learning Program.
Institution: University of Wyoming
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2112426216