Learning Task Knowledge and its Scope of Applicability in Experience-Based Planning Domains
Abstract
Experience-based planning domains (EBPDs) have been recently proposed to improve problem solving by learning from experience. EBPDs provide important concepts for long-term learning and planning in robotics. They rely on acquiring and using task knowledge, i.e., activity schemata, for generating concrete solutions to problem instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we extend previous work to generate a set of conditions as the scope of applicability for an activity schema. The inferred scope is a bounded representation of a set of problems of potentially unbounded size, in the form of a 3-valued logical structure, which allows an EBPD system to automatically find an applicable activity schema for solving task problems. We demonstrate the utility of our approach in a set of classes of problems in a simulated domain and a class of real world tasks in a fully physically simulated PR2 robot in Gazebo.
Cite
@article{arxiv.1902.10770,
title = {Learning Task Knowledge and its Scope of Applicability in Experience-Based Planning Domains},
author = {Vahid Mokhtari and Luis Seabra Lopes and Armando Pinho and Roman Manevich},
journal= {arXiv preprint arXiv:1902.10770},
year = {2019}
}
Comments
25 pages, 6 figures, 6 tables, 1 algorithm, 6 listings