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.
翻译:在本文中,我们提出了一个基于高光学的机器学习算法,一种以数据结构驱动的维护方法,以及一种基于Dijkstra算法概率应用的规划算法。它们共同形成了一个自主学习代理器的目标不可知的自动规划引擎,该代理器包含古典机器学习和传统人工智能的有益特性。我们证明,该算法决定了问题空间内的最佳解决方案、数学约束式学习绩效,并通过对学习曲线、目标实现率和对抽象和不确定性的反应作出明确预测的时间来提供数学模型分析系统进展状况。 为了验证绩效,我们展示了将代理器应用于三个古老的规划问题的结果,包括综合等级领域,并突出介绍了在分析中所阐明的属性的经验调查结果。