Publications

@misc{kwon_astra_2025,

title = {ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning through Action in Dynamic Offer Optimization},

author = {Deuksin Kwon and Jiwon Hae and Emma Clift and Daniel Shamsoddini and Jonathan Gratch and Gale M. Lucas},

url = {http://arxiv.org/abs/2503.07129},

doi = {10.48550/arXiv.2503.07129},

year  = {2025},

date = {2025-03-01},

urldate = {2025-03-18},

publisher = {arXiv},

abstract = {Negotiation requires dynamically balancing self-interest and cooperation to maximize one's own utility. Yet, existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning. To address this, we introduce principle-driven negotiation agents, powered by ASTRA, a novel framework for turn-level offer optimization grounded in two core principles: opponent modeling and Tit-for-Tat reciprocity. ASTRA operates in three stages: (1) interpreting counterpart behavior, (2) optimizing counteroffers via a linear programming (LP) solver, and (3) selecting offers based on negotiation tactics and the partner's acceptance probability. Through simulations and human evaluations, our agent effectively adapts to an opponent's shifting stance and achieves favorable outcomes through enhanced adaptability and strategic reasoning. Beyond improving negotiation performance, it also serves as a powerful coaching tool, offering interpretable strategic feedback and optimal offer recommendations.},

note = {arXiv:2503.07129 [cs]},

keywords = {Virtual Agents},

pubstate = {published},

tppubtype = {misc}

}

@misc{lin_estuary_2024,

title = {Estuary: A Framework For Building Multimodal Low-Latency Real-Time Socially Interactive Agents},

author = {Spencer Lin and Basem Rizk and Miru Jun and Andy Artze and Caitlin Sullivan and Sharon Mozgai and Scott Fisher},

url = {http://arxiv.org/abs/2410.20116},

doi = {10.1145/3652988.3696198},

year  = {2024},

date = {2024-10-01},

urldate = {2024-12-06},

abstract = {The rise in capability and ubiquity of generative artificial intelligence (AI) technologies has enabled its application to the field of Socially Interactive Agents (SIAs). Despite rising interest in modern AI-powered components used for real-time SIA research, substantial friction remains due to the absence of a standardized and universal SIA framework. To target this absence, we developed Estuary: a multimodal (text, audio, and soon video) framework which facilitates the development of low-latency, real-time SIAs. Estuary seeks to reduce repeat work between studies and to provide a flexible platform that can be run entirely off-cloud to maximize configurability, controllability, reproducibility of studies, and speed of agent response times. We are able to do this by constructing a robust multimodal framework which incorporates current and future components seamlessly into a modular and interoperable architecture.},

note = {arXiv:2410.20116 [cs]},

keywords = {Virtual Agents},

pubstate = {published},

tppubtype = {misc}

}