# Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments

## TL;DR

This paper presents general methods and architectural mechanisms for detecting, characterizing, and accommodating novelties in open-world environments, demonstrated through an adversarial multi-agent Monopoly game.

## Contribution

It introduces novel logical-based methods for novelty detection and adaptation, enhancing agent robustness in dynamic, adversarial settings.

## Key findings

- High novelty detection rates across various types
- Effective accommodation of rule and capability changes
- Validated in adversarial Monopoly environment

## Abstract

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. Certain novelties (e.g., changes in environment dynamics) can interfere with the performance or prevent agents from accomplishing task goals altogether. In this paper, we introduce general methods and architectural mechanisms for detecting and characterizing different types of novelties, and for building an appropriate adaptive model to accommodate them utilizing logical representations and reasoning methods. We demonstrate the effectiveness of the proposed methods in evaluations performed by a third party in the adversarial multi-agent board game Monopoly. The results show high novelty detection and accommodation rates across a variety of novelty types, including changes to the rules of the game, as well as changes to the agent's action capabilities.