With the impressive progress of deep learning, applications relying on machine learning are increasingly being integrated into daily life. However, most deep learning models have an opaque, oracle-like nature making it difficult to interpret and understand their decisions. This problem led to the development of the field known as eXplainable Artificial Intelligence (XAI). One method in this field known as Projective Simulation (PS) models a chain-of-thought as a random walk of a particle on a graph with vertices that have concepts attached to them. While this description has various benefits, including the possibility of quantization, it cannot be naturally used to model thoughts that combine several concepts simultaneously. To overcome this limitation, we introduce Multi-Excitation Projective Simulation (mePS), a generalization that considers a chain-of-thought to be a random walk of several particles on a hypergraph. A definition for a dynamic hypergraph is put forward to describe the agent's training history along with applications to AI and hypergraph visualization. An inductive bias inspired by the remarkably successful few-body interaction models used in quantum many-body physics is formalized for our classical mePS framework and employed to tackle the exponential complexity associated with naive implementations of hypergraphs. We prove that our inductive bias reduces the complexity from exponential to polynomial, with the exponent representing the cutoff on how many particles can interact. We numerically apply our method to two toy environments and a more complex scenario modelling the diagnosis of a broken computer. These environments demonstrate the resource savings provided by an appropriate choice of inductive bias, as well as showcasing aspects of interpretability. A quantum model for mePS is also briefly outlined and some future directions for it are discussed.

翻译：随着深度学习的显著进步，依赖机器学习的应用正日益融入日常生活。然而，大多数深度学习模型具有不透明、类神谕的特性，使其决策难以解释和理解。这一问题催生了可解释人工智能领域的发展。该领域中一种称为投影模拟的方法，将思维链建模为粒子在带有概念顶点的图上的随机游走。尽管这种描述具有多种优势（包括量子化的可能性），但它无法自然地用于模拟同时结合多个概念的思维。为克服这一限制，我们引入了多激发投影模拟，该推广将思维链视为多个粒子在超图上的随机游走。我们提出了动态超图的定义来描述智能体的训练历史，并探讨了其在人工智能和超图可视化中的应用。受量子多体物理中极为成功的少体相互作用模型启发，我们为经典多激发投影模拟框架形式化了一种归纳偏置，并利用其应对朴素超图实现所伴随的指数级复杂度。我们证明该归纳偏置可将复杂度从指数级降至多项式级，其中指数代表可相互作用粒子数量的截断上限。我们在数值上将本方法应用于两个玩具环境和一个模拟计算机故障诊断的更复杂场景。这些环境展示了适当选择归纳偏置所带来的资源节约优势，同时体现了可解释性的多个方面。本文还简要概述了多激发投影模拟的量子模型，并讨论了其未来发展方向。