Improving agent team performance through helper agents

Marie D. Manner, Maria L Gini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, we consider the problem of environmental constraints on teams and examine how the constraints impact teamwork. We simulate a realistic problem for teams in the physical world by implementing a limit on the range of communication among agents. Using BlocksWorld for Teams from Delft University of Technology, we build teams of 3-4 agents with varying communication ranges and of different agent type (regular task-performer, communication-only or reconnaissance-only) to test team performance in the constrained environment. We analyze the results and discuss implications to the team mental model. We show that adding a helper agent to the team can reduce task completion time, but not for all types of agents and environments.

Original languageEnglish (US)
Title of host publicationCognitive Agents for Virtual Environments - First International Workshop, CAVE 2012, Held at AAMAS 2012, Revised Selected Papers
Pages89-105
Number of pages17
DOIs
StatePublished - 2013
Event1st International Workshop on Cognitive Agents for Virtual Environments, CAVE 2012, Held at AAMAS 2012 - Valencia, Spain
Duration: Jun 4 2012Jun 4 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7764 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Cognitive Agents for Virtual Environments, CAVE 2012, Held at AAMAS 2012
CountrySpain
CityValencia
Period6/4/126/4/12

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    Manner, M. D., & Gini, M. L. (2013). Improving agent team performance through helper agents. In Cognitive Agents for Virtual Environments - First International Workshop, CAVE 2012, Held at AAMAS 2012, Revised Selected Papers (pp. 89-105). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7764 LNAI). https://doi.org/10.1007/978-3-642-36444-0_6