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.
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