Characterizing Novelty in the Military Domain

TL;DR

This paper discusses the importance of robustness to novelty in AI agents within military environments, proposing a domain-independent ontology to classify and evaluate different types of novelty for improving agent design.

Contribution

It introduces a formal ontology for characterizing military domain novelty, enabling systematic testing and development of robust AI agents against unseen challenges.

Findings

Mapping military novelty to a domain-independent ontology

Enabling measurement of agent detection and adaptation to novelty

Supporting development of robust AI for mission-critical environments

Abstract

A critical factor in utilizing agents with Artificial Intelligence (AI) is their robustness to novelty. AI agents include models that are either engineered or trained. Engineered models include knowledge of those aspects of the environment that are known and considered important by the engineers. Learned models form embeddings of aspects of the environment based on connections made through the training data. In operation, however, a rich environment is likely to present challenges not seen in training sets or accounted for in engineered models. Worse still, adversarial environments are subject to change by opponents. A program at the Defense Advanced Research Project Agency (DARPA) seeks to develop the science necessary to develop and evaluate agents that are robust to novelty. This capability will be required, before AI has the role envisioned within mission critical environments. As part of the Science of AI and Learning for Open-world Novelty (SAIL-ON), we are mapping possible military domain novelty types to a domain-independent ontology developed as part of a theory of novelty. Characterizing the possible space of novelty mathematically and ontologically will allow us to experiment with agent designs that are coming from the DARPA SAIL-ON program in relevant military environments. Utilizing the same techniques as being used in laboratory experiments, we will be able to measure agent ability to detect, characterize, and accommodate novelty.