Title

Virtual Facilitation of Human Group Interaction employing a Learning Classifier System with Crowdsourced Feedback

Presenter Information

Matthew Nuckolls

Department

Computer Science

Major

Computer Science

Research Advisor

Tauritz, Daniel R.
Luechtefeld, Ray

Advisor's Department

Computer Science

Second Advisor's Department

Engineering Management and Systems Engineering

Abstract

Natural human group dynamics sometimes can lead a group down unproductive pathways. An expert group facilitator may need to intervene to return the group to a productive workflow. However, human expert group facilitators are scarce and prohibitively expensive. We can codify the circumstances that lead the group astray into a set of matching rules, with an appropriate intervention for each situation. This proposal is concerned with developing a Virtual Facilitator software system which employs a Learning Classifier System to evolve increasingly higher quality matching rules based on crowdsourced feedback. Such a Virtual Facilitator can replace human expert group facilitators at a fraction of the cost and be ubiquitously available.

Biography

Matthew Nuckolls is scheduled to graduate from Missouri S&T in May 2010 with a B.S. in Computer Science and a minor in Cognitive Neuropsychology. He is an undergraduate researcher in the Natural Computation Laboratory, studying uses of Learning Classifier Systems to solve real-world problems. In addition to his research work, Matthew tutors and grades for the course CmpSc 253 - Algorithms. Prior to his academic career, Matthew served in the US Air Force for 10 years as an Explosive Ordnance Disposal Technician.

Research Category

Research Proposals

Presentation Type

Poster Presentation

Document Type

Poster

Location

Upper Atrium/Hallway

Presentation Date

07 Apr 2010, 9:00 am - 11:45 am

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Apr 7th, 9:00 AM Apr 7th, 11:45 AM

Virtual Facilitation of Human Group Interaction employing a Learning Classifier System with Crowdsourced Feedback

Upper Atrium/Hallway

Natural human group dynamics sometimes can lead a group down unproductive pathways. An expert group facilitator may need to intervene to return the group to a productive workflow. However, human expert group facilitators are scarce and prohibitively expensive. We can codify the circumstances that lead the group astray into a set of matching rules, with an appropriate intervention for each situation. This proposal is concerned with developing a Virtual Facilitator software system which employs a Learning Classifier System to evolve increasingly higher quality matching rules based on crowdsourced feedback. Such a Virtual Facilitator can replace human expert group facilitators at a fraction of the cost and be ubiquitously available.