Learning the Scope of Applicability for Task Planning Knowledge in Experience-Based Planning Domains
Vahid Mokhtari, Roman Manevich, Luís Seabra Lopes, Armando J. Pinho
Abstract
Experience-based planning domains (EBPDs) have been proposed to improve problem solving by learning from experience. They rely on acquiring and using task knowledge, i.e., activity schemata, for generating solutions to problem instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we extend our previous work to generate a set of conditions that determine the scope of applicability of an activity schema. The inferred scope is an abstract representation of a potentially unbounded set of problems, in the form of a 3-valued logical structure, which is used to test the applicability of the respective activity schema for solving different task problems. We validate this work on two classical planning domains and a simulated PR2 in Gazebo.
BibTeX
@inproceedings{iros2019_learningthescope,
title = {Learning the Scope of Applicability for Task Planning Knowledge in Experience-Based Planning Domains},
author = {Vahid Mokhtari and Roman Manevich and Luís Seabra Lopes and Armando J. Pinho},
booktitle = {IROS 2019},
year = {2019}
}