Collaborative plans for complex group action
Artificial Intelligence
Temporal Reasoning for a Collaborative Planning Agent in a Dynamic Environment
Annals of Mathematics and Artificial Intelligence
Using Guidelines to Constrain Interactive Case-Based HTN Planning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Adaptive Agent Integration Architectures for Heterogeneous Team Members
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Using Cooperative Mediation to Solve Distributed Constraint Satisfaction Problems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Introduction to Group Work Practice (with MyHelpingLab), An (5th Edition)
Introduction to Group Work Practice (with MyHelpingLab), An (5th Edition)
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Building and refining abstract planning cases by change of representation language
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Group planning with time constraints
Annals of Mathematics and Artificial Intelligence
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In this paper we present a lightweight teamwork implementation by using abstraction hierarchies. The basis of this implementation is ADAPT, which supports Autonomous Dynamic Agent Planning for Teamwork. ADAPT's novelty stems from how it succinctly decomposes teamwork problems into two separate planners: a task network for the set of activities to be performed by a specific agent and a separate group network for addressing team organization factors. Because abstract search techniques are the basis for creating these two components, ADAPT agents are able to effectively address teamwork in dynamic environments without explicitly enumerating the entire set of possible team states. During run-time, ADAPT agents then expand the teamwork states that are necessary for task completion through an association algorithm to dynamically link its task and group planners. As a result, ADAPT uses far fewer team states than existing teamwork models. We describe how ADAPT was implemented within a commercial training and simulation application, and present evidence detailing its success in concisely and effectively modeling teamwork.