Human-agent collaboration: A goal-based BDI approach
conference contribution
posted on 2020-02-19, 00:00 authored by Salma NoorunnisaSalma Noorunnisa, Jacqueline JarvisJacqueline Jarvis, M Watson, Dennis JarvisDennis JarvisThe Belief-Desire-Intention (BDI) model of agency has been a popular choice for the modelling of goal-based behaviour for both individual agents and more recently, teams of agents. Numerous frameworks have been developed since the model was first proposed in the early 1980s. However, while the more recent frameworks support a delegative model of agent/agent and human/agent collaboration, no frameworks support a general model of collaboration. Given the importance of collaboration in the development of practical semi-autonomous agent applications, we consider this to constitute a major limitation of traditional BDI frameworks. In this paper, we present GORITE, a novel BDI framework that by employing explicit goal representations, overcomes many of the limitations of traditional frameworks. In terms of human/agent collaboration, key requirements are identified and through the use of a representative but simple example, the ability of GORITE to address those requirements is demonstrated. © Springer International Publishing AG, part of Springer Nature 2019.
History
Editor
Jezic G; Chen-Burger Y-HJ; Howlett RJ; Jain LC; Vlacic L; Sperka RVolume
SIST 96Start Page
3End Page
12Number of Pages
10Start Date
2018-06-20Finish Date
2018-06-22eISSN
2190-3026ISSN
2190-3018ISBN-13
9783319920306Location
Gold Coast, AustraliaPublisher
SpringerPlace of Publication
Cham, SwitzerlandPublisher DOI
Peer Reviewed
- Yes
Open Access
- No
External Author Affiliations
University of QueenslandAuthor Research Institute
- Centre for Intelligent Systems
Era Eligible
- Yes
Name of Conference
12th International Conference on Agents and Multi-Agent Systems: Technologies and Applications (KES-AMSTA-18)Usage metrics
Categories
Licence
Exports
RefWorksRefWorks
BibTeXBibTeX
Ref. managerRef. manager
EndnoteEndnote
DataCiteDataCite
NLMNLM
DCDC