Enabling autonomous underwater vehicles to reason hypothetically

Abstract

In this paper, we report on work that applies a form of artificial intelligence (AI) to autonomous underwater vehicle (AUV) operations. Called “language-centered intelligence” (LCI), this form of AI uses hypothetical reasoning to build contingency plans that enable AUVs to proactively anticipate changes in mission circumstance. After describing the control architecture we have used to embed LCI in our fleet of AUVs, we present an application of LCI to the problem of vehicle loss. The specific solution to this problem operates at two levels. It is assumed that a standard replacement approach will be used to direct vehicle replacement in normal circumstances, while a higher-level, so-called “imagination” replacement approach that uses LCI will direct replacement in more extraordinary circumstances. First, though, what differentiates normal from extraordinary circumstances must be identified. Preliminary results from an experiment designed to distinguish normal from extraordinary replacement circumstances are presented.