Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies (bibtex)
by Ahana Ghosh, Sebastian Tschiatschek, Hamed Mahdavi, Adish Singla
Abstract:
We study the problem of designing AI agents that can robustly cooperate with people in human-machine partnerships. Our work is inspired by real-life scenarios in which an AI agent, e.g., a virtual assistant, has to cooperate with new users after its deployment. We model this problem via a parametric MDP framework where the parameters correspond to a user's type and characterize her behavior. In the test phase, the AI agent has to interact with a user of unknown type. Our approach to designing a robust AI agent relies on observing the user's actions to make inferences about the user's type and adapting its policy to facilitate efficient cooperation. We show that without being adaptive, an AI agent can end up performing arbitrarily bad in the test phase. We develop two algorithms for computing policies that automatically adapt to the user in the test phase. We demonstrate the effectiveness of our approach in solving a two-agent collaborative task.
Reference:
Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive PoliciesAhana Ghosh, Sebastian Tschiatschek, Hamed Mahdavi, Adish Singla In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) 2020.
Bibtex Entry:
@inproceedings{ghosh2019deployment,
    title={Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies},
    author={Ahana Ghosh and Sebastian Tschiatschek and Hamed Mahdavi and Adish Singla},
    year={2020},
    booktitle={International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
    url = {http://ifaamas.org/Proceedings/aamas2020/pdfs/p447.pdf},
    abstract = {We study the problem of designing AI agents that can robustly cooperate with people in human-machine partnerships. Our work is inspired by real-life scenarios in which an AI agent, e.g., a virtual assistant, has to cooperate with new users after its deployment. We model this problem via a parametric MDP framework where the parameters correspond to a user's type and characterize her behavior. In the test phase, the AI agent has to interact with a user of unknown type. Our approach to designing a robust AI agent relies on observing the user's actions to make inferences about the user's type and adapting its policy to facilitate efficient cooperation. We show that without being adaptive, an AI agent can end up performing arbitrarily bad in the test phase. We develop two algorithms for computing policies that automatically adapt to the user in the test phase. We demonstrate the effectiveness of our approach in solving a two-agent collaborative task.},
    tag = {SDM}
}
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