Title

Learning in context: enhancing machine learning with context-based reasoning

Authors

Authors

G. Stein;A. J. Gonzalez

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

Appl. Intell.

Keywords

Machine learning from observation; Context-based reasoning; Learning; from demonstration; Learning from imitation; DIVIDE-AND-CONQUER; PARTICLE SWARM OPTIMIZATION; HUMAN-PERFORMANCE; NEURAL NETWORKS; BEHAVIOR; DISCOVERY; Computer Science, Artificial Intelligence

Abstract

This article describes how an experiment to train an agent to perform a task, which had originally failed, was made successful by incorporating a contextual structure that decomposed the tasks into contexts through Context-based Reasoning. The task involved a simulation of a crane that was used by a human operator to move boxes from arbitrary locations throughout a wide area to a designated drop off location in the environment. Initial attempts to teach an agent how to perform the task through observation in a context-free manner yielded poor performance. However, when the task to be learned was decomposed into separate contexts and the agents learned each context independently, the performance improved significantly. The paper describes the process that enabled the improvements achieved and discusses the tests and results that demonstrated the improvement.

Journal Title

Applied Intelligence

Volume

41

Issue/Number

3

Publication Date

1-1-2014

Document Type

Article

Language

English

First Page

709

Last Page

724

WOS Identifier

WOS:000342426700004

ISSN

0924-669X

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