  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.
