Sriram Gopalakrishnan, Ph.D.

Sriram Gopalakrishnan, Ph.D.

AI Research Lead at J.P. Morgan Chase | Sequential-Decision Making Problems, Human-AI Interaction, & Generative AI

Recognizing Plans by Learning Embeddings from Observed Action Distributions

Published in Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), 2018

Recommended citation: Zha, Y., Li, Y., Gopalakrishnan, S., Li, B., & Kambhampati, S. (2018). Recognizing Plans by Learning Embeddings from Observed Action Distributions. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. https://dl.acm.org/doi/10.5555/3237383.3237976

Abstract

Traditional plan recognition requires explicit domain schemas. We present a representation learning approach that maps observed sequential actions into embedding spaces where goal hypotheses can be scored using metric proximity.

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Recommended citation: Zha, Y., Li, Y., Gopalakrishnan, S., Li, B., & Kambhampati, S. (2018). Recognizing Plans by Learning Embeddings from Observed Action Distributions. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems.