A hybrid agent architecture for modeling autonomous agents in SAGE

Tariq, Amina, Basharat, Amna, Ahmad, H. Farooq, Suguri, Hiroki, & Ali, Arshad (2005) A hybrid agent architecture for modeling autonomous agents in SAGE. In Gallagher, Marcus, Hogan, James P., & Maire, Frederic (Eds.) Intelligent Data Engineering and Automated Learning - IDEAL 2005. Springer, Berlin Heidelberg, pp. 478-485.

View at publisher


This paper highlights the Hybrid agent construction model being developed that allows the description and development of autonomous agents in SAGE (Scalable, fault Tolerant Agent Grooming Environment) - a second generation FIPA-Compliant Multi-Agent system. We aim to provide the programmer with a generic and well defined agent architecture enabling the development of sophisticated agents on SAGE, possessing the desired properties of autonomous agents - reactivity, pro-activity, social ability and knowledge based reasoning. © Springer-Verlag Berlin Heidelberg 2005.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 93921
Item Type: Book Chapter
Additional URLs:
Keywords: Agent Architecture, Autonomous agents, Hybrid Agent, Intelligent Agent, MAS
DOI: 10.1007/11508069_62
ISBN: 9783540316930
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Deposited On: 15 Apr 2016 01:05
Last Modified: 27 Jun 2017 08:03

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page