no code implementations • 28 Feb 2023 • Tung Thai, Ming Shen, Mayank Garg, Ayush Kalani, Nakul Vaidya, Utkarsh Soni, Mudit Verma, Sriram Gopalakrishnan, Neeraj Varshney, Chitta Baral, Subbarao Kambhampati, Jivko Sinapov, Matthias Scheutz
Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task performance.
no code implementations • 27 Oct 2022 • Utkarsh Soni, Nupur Thakur, Sarath Sreedharan, Lin Guan, Mudit Verma, Matthew Marquez, Subbarao Kambhampati
If the relevant concept is not in the shared vocabulary, then it is learned.
no code implementations • 9 Jul 2021 • Sriram Gopalakrishnan, Utkarsh Soni, Tung Thai, Panagiotis Lymperopoulos, Matthias Scheutz, Subbarao Kambhampati
The game of monopoly is an adversarial multi-agent domain where there is no fixed goal other than to be the last player solvent, There are useful subgoals like monopolizing sets of properties, and developing them.
no code implementations • 23 Jun 2021 • Utkarsh Soni, Sarath Sreedharan, Subbarao Kambhampati
The former is achieved by a data-driven clustering approach while for the latter, we compile our explanation generation problem into a POMDP.
no code implementations • 15 Feb 2020 • Sriram Gopalakrishnan, Utkarsh Soni
Learning the preferences of a human improves the quality of the interaction with the human.
no code implementations • ICLR 2022 • Sarath Sreedharan, Utkarsh Soni, Mudit Verma, Siddharth Srivastava, Subbarao Kambhampati
As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions.