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.

本研究提出了一种基于超图的机器学习算法，一种数据结构驱动的维护方法，以及一种基于概率应用 Dijkstra 算法的规划算法，从而形成了一个目标不可知的自动化学习代理计划引擎，该引擎结合了经典机器学习和传统人工智能的有益特性。我们证明了该算法能够在问题空间内确定最优解，在数学上限制了学习性能，并提供了一种数学模型，分析了系统状态在时间上的进展，从而得出了学习曲线、目标实现率和抽象和不确定性响应的明确预测。为了验证性能，我们展示了将代理应用于三个典型的计划问题的结果，并突出了阐明的特性的实证结论。