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展，依赖机器学习的技术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、类似神谕的特性，难以解释和理解其决策。这一问题催生了被称为可解释人工智能（XAI）的研究领域。其中一种方法——投影模拟（PS）——将思维链建模为粒子在带有概念标签的顶点图上的随机游走。虽然这种描述具有多种优势（包括量子化的可能性），但无法自然地用于建模同时结合多个概念的思维。为克服这一局限性，我们引入多激发投影模拟（mePS），这是一种将思维链视为多个粒子在超图上随机游走的泛化方法。我们提出了动态超图的定义，以描述智能体的训练历史，并展示了其在人工智能和超图可视化中的应用。受量子多体物理学中非常成功的少体相互作用模型启发，我们为经典mePS框架形式化了一种归纳偏置，并利用其应对超图朴素实现中指数级复杂性的挑战。我们证明，该归纳偏置将复杂性从指数级降低至多项式级，其指数表示允许相互作用的粒子数截断。我们通过两个玩具环境以及一个更复杂的计算机故障诊断场景对方法进行数值验证。这些实验展示了适当选择归纳偏置所带来的资源节省，并突显了可解释性的优势。此外，我们简要概述了mePS的量子模型，并讨论了其未来发展方向。