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.

通过建立一个灵感来自 Thinking Fast and Slow 的认知理论的系统架构 SOFAI，该系统可以在不同情境下解决计划问题， 实现快速和慢思维，并且最终证明这种新的智能系统在问题的广泛性、解决时间和解决方案准确性等方面具有更好的表现。