Neurosymbolic system profiling: A template-based approach

Abstract

Two antagonistic categories can be identified within Artificial Intelligence models: symbolic and subsymbolic. Neurosymbolic artificial intelligence is the term used to describe a set of approaches whose goal is to combine both symbolic and subsymbolic models under a unified approach. Design methods for neurosymbolic systems have been presented throughout the years, focusing mostly on theoretical aspects, such as how knowledge is represented within the system or which learning paradigm should be used. However, existing design approaches do not consider contextual or practical aspects. This work presents a template-based approach for the design of neurosymbolic systems, focusing specifically on the interaction between knowledge-based systems (symbolic) and deep learning models (subsymbolic). The proposed method extends previously addressed aspects (limitations and benefits) with contextual and practical aspects (restrictions and considerations). From this general four-dimensional descriptive template, a specific template is provided describing the parameters of each potential integration between knowledge-based systems and deep learning. Instantiation examples are provided to demonstrate the versatility and applicability of the proposal. In addition, this paper presents an extensive review of the current state-of-the-art in the design of neurosymbolic systems. The benefits of the proposed approach are two-fold. First, it enables high-level description of existing neurosymbolic systems, thus facilitating the search process for research. Secondly, the proposed method simplifies the engineering process and reduces development time because: (i) previously existing instances that are already described by templates can be easily reused, or (ii) the corresponding integration template can serve as a baseline to develop new systems from scratch.