  In this paper, we present a hypergraph--based machine learning algorithm, a
datastructure--driven maintenance method, and a planning algorithm based on a
probabilistic application of Dijkstra's algorithm. Together, these form a goal
agnostic automated planning engine for an autonomous learning agent which
incorporates beneficial properties of both classical Machine Learning and
traditional Artificial Intelligence. We prove that the algorithm determines
optimal solutions within the problem space, mathematically bound learning
performance, and supply a mathematical model analyzing system state progression
through time yielding explicit predictions for learning curves, goal
achievement rates, and response to abstractions and uncertainty. To validate
performance, we exhibit results from applying the agent to three archetypal
planning problems, including composite hierarchical domains, and highlight
empirical findings which illustrate properties elucidated in the analysis.
