Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments

Tung Thai, Mudit Verma, Utkarsh Soni, Sriram Gopalakrishnan, Ming Shen, Mayank Garg, Ayush Kalani, Nakul Vaidya, Neeraj Varshney, Chitta Baral, Subbarao Kambhampati, Jivko Sinapov, Matthias Scheutz

Abstract: Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to achieve satisfactory task performance. We sketch general methods for detecting and characterizing different types of novelties, and for building an appropriate adaptive model to accommodate them utilizing logical representations and reasoning methods in stochastic partially observable multi-agent environments. We also briefly report results from evaluations of our algorithms in the game domain of Monopoly. The results show high novelty detection and accommodation rates.