Extending the recognition-primed decision model to support human-agent collaboration

Xiaocong Fan, Shuang Sun, Michale McNeese, John Yen

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

11 Citations (Scopus)

Abstract

There has been much research investigating team cognition, naturalistic decision making, and collaborative technology as it relates to real world, complex domains of practice. However, there has been limited work in incorporating naturalistic decision making models for supporting distributed team decision making. The aim of this research is to support human decision making teams using cognitive agents empowered by a collaborative Recognition-Primed Decision model. In this paper, we first describe an RPD-enabled agent architecture (R-CAST), in which we have implemented an internal mechanism of decision-making adaptation based on collaborative expectancy monitoring, and an information exchange mechanism driven by relevant cue analysis. We have evaluated R-CAST agents in a real-time simulation environment, feeding teams with frequent decision-making tasks under different tempo situations. While the result conforms to psychological findings that human team members are extremely sensitive to their workload in high-tempo situations, it clearly indicates that human teams, when supported by R-CAST agents, can perform better in the sense that they can maintain team performance at acceptable levels in high time pressure situations.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05
EditorsF. Dignum, V. Dignum, S. Koenig, S. Kraus, M. Pechoucek, M. Singh, D. Steiner, S. Thompson, M. Wooldridge
Pages1077-1084
Number of pages8
StatePublished - 2005
Event4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05 - Utrecht, Netherlands
Duration: Jul 25 2005Jul 29 2005

Other

Other4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05
CountryNetherlands
CityUtrecht
Period7/25/057/29/05

Fingerprint

Decision making
Monitoring

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Fan, X., Sun, S., McNeese, M., & Yen, J. (2005). Extending the recognition-primed decision model to support human-agent collaboration. In F. Dignum, V. Dignum, S. Koenig, S. Kraus, M. Pechoucek, M. Singh, D. Steiner, S. Thompson, ... M. Wooldridge (Eds.), Proceedings of the 4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05 (pp. 1077-1084)
Fan, Xiaocong ; Sun, Shuang ; McNeese, Michale ; Yen, John. / Extending the recognition-primed decision model to support human-agent collaboration. Proceedings of the 4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05. editor / F. Dignum ; V. Dignum ; S. Koenig ; S. Kraus ; M. Pechoucek ; M. Singh ; D. Steiner ; S. Thompson ; M. Wooldridge. 2005. pp. 1077-1084
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abstract = "There has been much research investigating team cognition, naturalistic decision making, and collaborative technology as it relates to real world, complex domains of practice. However, there has been limited work in incorporating naturalistic decision making models for supporting distributed team decision making. The aim of this research is to support human decision making teams using cognitive agents empowered by a collaborative Recognition-Primed Decision model. In this paper, we first describe an RPD-enabled agent architecture (R-CAST), in which we have implemented an internal mechanism of decision-making adaptation based on collaborative expectancy monitoring, and an information exchange mechanism driven by relevant cue analysis. We have evaluated R-CAST agents in a real-time simulation environment, feeding teams with frequent decision-making tasks under different tempo situations. While the result conforms to psychological findings that human team members are extremely sensitive to their workload in high-tempo situations, it clearly indicates that human teams, when supported by R-CAST agents, can perform better in the sense that they can maintain team performance at acceptable levels in high time pressure situations.",
author = "Xiaocong Fan and Shuang Sun and Michale McNeese and John Yen",
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pages = "1077--1084",
editor = "F. Dignum and V. Dignum and S. Koenig and S. Kraus and M. Pechoucek and M. Singh and D. Steiner and S. Thompson and M. Wooldridge",
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Fan, X, Sun, S, McNeese, M & Yen, J 2005, Extending the recognition-primed decision model to support human-agent collaboration. in F Dignum, V Dignum, S Koenig, S Kraus, M Pechoucek, M Singh, D Steiner, S Thompson & M Wooldridge (eds), Proceedings of the 4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05. pp. 1077-1084, 4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05, Utrecht, Netherlands, 7/25/05.

Extending the recognition-primed decision model to support human-agent collaboration. / Fan, Xiaocong; Sun, Shuang; McNeese, Michale; Yen, John.

Proceedings of the 4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05. ed. / F. Dignum; V. Dignum; S. Koenig; S. Kraus; M. Pechoucek; M. Singh; D. Steiner; S. Thompson; M. Wooldridge. 2005. p. 1077-1084.

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

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Fan X, Sun S, McNeese M, Yen J. Extending the recognition-primed decision model to support human-agent collaboration. In Dignum F, Dignum V, Koenig S, Kraus S, Pechoucek M, Singh M, Steiner D, Thompson S, Wooldridge M, editors, Proceedings of the 4th International Conference on Autonomous Agents and Multi agent Systems, AAMAS 05. 2005. p. 1077-1084