Title

A Framework For The Qualitative Comparison Of Diverse Developmental Agents

Abstract

Developmental agents learn task-nonspecific skills through environmental interactions. The humanlike flexibility of such agents isn't captured by domain-specific performance metrics. We present a novel framework that complements traditional metrics by allowing cross-domain comparison. The framework considers four properties of developmental agents: the degree of human involvement in design, the length of the agent's developmental period, the architectural support for acquiring new behaviors, and the tolerated dimensionality of input. This framework is applied to real-world systems in three case studies. We find that our framework allows cross-domain comparison that would not be contributed by traditional quantitative metrics. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

Publication Date

12-13-2013

Publication Title

FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference

Number of Pages

184-187

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84889770474 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84889770474

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