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Adaptive Agent Architecture for Real-time Human-Agent Teaming

Ni, Tianwei and Li, Huao and Agrawal, Siddharth and Raja, Suhas and Jia, Fan and Gui, Yikang and Hughes, Dana and Lewis, Michael and Sycara, Katia P. (2021) Adaptive Agent Architecture for Real-time Human-Agent Teaming. In: AAAI 2021 Workshop on Plan, Activity, and Intent Recognition.

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Teamwork is a set of interrelated reasoning, actions and behaviors of team members that facilitate common objectives. Teamwork theory and experiments have resulted in a set of states and processes for team effectiveness in both human-human and agent-agent teams. However, human-agent teaming is less well studied because it is so new and involves asymmetry in policy and intent not present in human teams. To optimize team performance in human-agent teaming, it is critical that agents infer human intent and adapt their polices for smooth coordination. Most literature in human-agent teaming builds agents referencing a learned human model. Though these agents are guaranteed to perform well with the learned model, they lay heavy assumptions on human policy such as optimality and consistency, which is unlikely in many real-world scenarios. In this paper, we propose a novel adaptive agent architecture in human-model-free setting on a twoplayer cooperative game, namely Team Space Fortress (TSF). Previous human-human team research have shown complementary policies in TSF game and diversity in human players’ skill, which encourages us to relax the assumptions on human policy. Therefore, we discard learning human models from human data, and instead use an adaptation strategy on a pre-trained library of exemplar policies composed of RL algorithms or rule-based methods with minimal assumptions of human behavior. The adaptation strategy relies on a novel similarity metric to infer human policy and then selects the most complementary policy in our library to maximize the team performance. The adaptive agent architecture can be deployed in real-time and generalize to any off-the-shelf static agents. We conducted human-agent experiments to evaluate the proposed adaptive agent framework, and demonstrated the suboptimality, diversity, and adaptability of human policies in human-agent teams.


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Item Type: Conference or Workshop Item (Paper)
Status: Published
CreatorsEmailPitt UsernameORCID
Ni, Tianwei
Li, Huao
Agrawal, Siddharth
Raja, Suhas
Jia, Fan
Gui, Yikang
Hughes, Dana
Lewis, Michaelcmlewis@pitt.educmlewis0000-0002-1013-9482
Sycara, Katia P.
Date: 2021
Date Type: Publication
Journal or Publication Title: CoRR PAIR Workshop
Volume: abs/21
Event Title: AAAI 2021 Workshop on Plan, Activity, and Intent Recognition
Event Type: Workshop
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Official URL:
Date Deposited: 13 Aug 2021 20:22
Last Modified: 13 Aug 2021 20:22


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