Realistic cognitive load modeling for enhancing shared mental models in human-agent collaboration

Xiaocong Fan, John Yen

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

6 Citations (Scopus)

Abstract

Human team members often develop shared expectations to predict each other's needs and coordinate their behaviors. In this paper the concept "Shared Belief Map" is proposed as a basis for developing realistic shared expectations among a team of Human-Agent-Pairs (HAPs). The establishment of shared belief maps relies on inter-agent information sharing, the effectiveness of which highly depends on agents' processing loads and the instantaneous cognitive loads of their human partners. We investigate HMM-based cognitive load models to facilitate team members to "share the right information with the right party at the right time". The shared belief map concept and the cognitive/processing load models have been implemented in a cognitive agent architecture - -SMMall. A series of experiments were conducted to evaluate the concept, the models, and their impacts on the evolving of shared mental models of HAP teams.

Original languageEnglish (US)
Title of host publicationAAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems
Pages395-402
Number of pages8
DOIs
StatePublished - Dec 1 2007
Event6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'07 - Honolulu, HI, United States
Duration: May 14 2008May 18 2008

Publication series

NameProceedings of the International Conference on Autonomous Agents

Other

Other6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'07
CountryUnited States
CityHonolulu, HI
Period5/14/085/18/08

Fingerprint

Cognitive Load
Mental Models
Modeling
Concept Maps
Cognitive Architecture
Agent Architecture
Information Sharing
Processing
Instantaneous
Model
Predict
Series
Human
Collaboration
Evaluate
Experiment
Beliefs
Experiments
Concepts

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Theoretical Computer Science

Cite this

Fan, X., & Yen, J. (2007). Realistic cognitive load modeling for enhancing shared mental models in human-agent collaboration. In AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 395-402). [60] (Proceedings of the International Conference on Autonomous Agents). https://doi.org/10.1145/1329125.1329197
Fan, Xiaocong ; Yen, John. / Realistic cognitive load modeling for enhancing shared mental models in human-agent collaboration. AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. 2007. pp. 395-402 (Proceedings of the International Conference on Autonomous Agents).
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Fan, X & Yen, J 2007, Realistic cognitive load modeling for enhancing shared mental models in human-agent collaboration. in AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems., 60, Proceedings of the International Conference on Autonomous Agents, pp. 395-402, 6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'07, Honolulu, HI, United States, 5/14/08. https://doi.org/10.1145/1329125.1329197

Realistic cognitive load modeling for enhancing shared mental models in human-agent collaboration. / Fan, Xiaocong; Yen, John.

AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. 2007. p. 395-402 60 (Proceedings of the International Conference on Autonomous Agents).

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

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Fan X, Yen J. Realistic cognitive load modeling for enhancing shared mental models in human-agent collaboration. In AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. 2007. p. 395-402. 60. (Proceedings of the International Conference on Autonomous Agents). https://doi.org/10.1145/1329125.1329197