University of Tasmania
Browse
AISAT04Final.pdf (167 kB)

Expert Software Support for Ad-hoc Teams - Enabling Online Interaction Rules

Download (167 kB)
conference contribution
posted on 2023-05-26, 07:42 authored by Kildare, RA, Williams, R, Hartnett, J
An expert set of interaction rules may help to guide leaderless, transient teams of individuals that work online and asynchronously. Medical and engineering professionals as well as students fall into this category. Sufficient evidence exists to treat such teams as complex systems, to consider team processes as emergent and thus unpredictable. For software to support such teams in a variety of scenarios, the components must cater for the emergence of interaction rules. A software "Moderator" is proposed which gathers, monitors and executes these rules. The standard AI methodology of expert systems will be used. Symbolic rules link the attribute value pairs that software can monitor with language that is meaningful to the team members. The reuse of rules to form new rules allows for a process of evolution. Multiple Classification Ripple Down Rules (MCRDR) provides a mechanism for team-member control over the evolution of rules. One end result proposed is the collection of the rules developed by similar teams as an expert set of rules for the context in which the teams work. This may fulfil the traditional role of a human expert where one is unlikely to exist.

History

Publication status

  • Published

Event title

Artificial Intelligence in Science and Technology, AISAT-2004

Event Venue

Hobart, Tasmania, Australia

Date of Event (Start Date)

2004-11-21

Date of Event (End Date)

2004-11-25

Repository Status

  • Open

Usage metrics

    University Of Tasmania

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC