The concept of Artificial Intelligence has gained a lot of attention over the last decade. In particular, AI-based tools have been employed in several scenarios and are, by now, pervading our everyday life. Nonetheless, most of these systems lack many capabilities that we would naturally consider to be included in a notion of "intelligence". In this work, we present an architecture that, inspired by the cognitive theory known as Thinking Fast and Slow by D. Kahneman, is tasked with solving planning problems in different settings, specifically: classical and multi-agent epistemic. The system proposed is an instance of a more general AI paradigm, referred to as SOFAI (for Slow and Fast AI). SOFAI exploits multiple solving approaches, with different capabilities that characterize them as either fast or slow, and a metacognitive module to regulate them. This combination of components, which roughly reflects the human reasoning process according to D. Kahneman, allowed us to enhance the reasoning process that, in this case, is concerned with planning in two different settings. The behavior of this system is then compared to state-of-the-art solvers, showing that the newly introduced system presents better results in terms of generality, solving a wider set of problems with an acceptable trade-off between solving times and solution accuracy.

翻译：在过去的十年中，人工智能概念引起了广泛关注。特别是，基于人工智能的工具已在多种场景中得到应用，并已渗透到我们的日常生活中。然而，这些系统大多缺乏我们通常认为应包含在“智能”概念中的许多能力。本文提出了一种受D. Kahneman的认知理论“思考，快与慢”启发的架构，旨在解决不同环境下的规划问题，具体包括：经典规划与多智能体认知规划。所提出的系统是一种更通用的人工智能范式——SOFAI（慢速与快速人工智能）的实例。SOFAI利用多种求解方法，这些方法具有不同的能力，分别被定性为“快速”或“缓慢”，并配备了一个元认知模块来调控它们。这种组件组合大致反映了D. Kahneman所描述的人类推理过程，使我们能够增强推理过程，在此场景下，其涉及两种不同环境下的规划。随后，将该系统的行为与现有最先进的求解器进行比较，结果表明，新引入的系统在通用性方面表现更佳，能够解决更广泛的问题，并在求解时间与解准确性之间取得了可接受的权衡。