Work

Symbol acquisition for probabilistic high-level planning

Year: 2015

Type: conference-paper

Abstract: We introduce a framework that enables an agent to autonomously learn its own symbolic representation of a low-level, continuous environment. Propositional symbols are formalized as names for probability distributions, providing a natural means of dealing with uncertain representations and probabilistic plans. We determine the symbols that are sufficient for computing the probability with which a plan will succeed, and demonstrate the acquisition of a symbolic representation in a computer game domain.

Source: DSpace@MIT (Massachusetts Institute of Technology)

Authors: Konidaris, George, Kaelbling, Leslie P, Lozano-Perez, Tomas

Institutions: Duke University, MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Office of Naval Research, Air Force Office of Scientific Research, Army Research Office

Awards: G3802117657, G8510421867, G8876996369

OpenAlex ID: W2222874733

FWCI: 3.043

Cites: 33

Cited by: 40

Related to: 10