For effective human-agent teaming, robots and other artificial intelligence
(AI) agents must infer their human partner's abilities and behavioral response
patterns and adapt accordingly. Most prior works make the unrealistic
assumption that one or more teammates can act near-optimally. In real-world
collaboration, humans and autonomous agents can be suboptimal, especially when
each only has partial domain knowledge. In this work, we develop computational
modeling and optimization techniques for enhancing the performance of
suboptimal human-agent teams, where the human and the agent have asymmetric
capabilities and act suboptimally due to incomplete environmental knowledge. We
adopt an online Bayesian approach that enables a robot to infer people's
willingness to comply with its assistance in a sequential decision-making game.
Our user studies show that user preferences and team performance indeed vary
with robot intervention styles, and our approach for mixed-initiative
collaborations enhances objective team performance ($p<.001$) and subjective
measures, such as user's trust ($p<.001$) and perceived likeability of the
robot ($p<.001$).

研究通过计算建模和优化技术，提升人工智能与人类队伍的协同性能，其中人类和机器智能代理由于环境知识不完整而发挥亚最优能力，并采用在线贝叶斯方法推断人们愿意接受机器的协助，实验结果显示协作方式与团队性能、用户信任和对机器的好感度存在显著关联。

在线贝叶斯适应下的次优混合人机团队合作

Mixed-Initiative Human-Robot Teaming under Suboptimality with Online  Bayesian Adaptation

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 two-player 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.

提出一种基于预训练的 RL 策略库和规则策略的人类无模型自适应代理架构，通过一种新的相似性度量方法推断人类策略并选择最具互补性的策略，以此最大化团队在人类机器协作中的总体表现。