This chapter provides an introduction to the field of hybrid neural systems. Hybrid neural
systems are computational systems which are based mainly on artificial neural networks but …

In this paper we present a broad overview of the last 40 years of research on cognitive
architectures. To date, the number of existing architectures has reached several hundred …

The performance of symbolic inference tasks has long been a challenge to connectionists. In
this paper, we present an extended survey of this area. Existing connectionist inference …

Can large language models produce expert‐quality philosophical texts? To investigate this,
we fine‐tuned GPT‐3 with the works of philosopher Daniel Dennett. To evaluate the model …

This chapter introduces the basics of neural-symbolic systems used thoughout the book. A
brief bibliographical review is also presented. Neural-symbolic systems have become a very …

This series will include monographs and collections of studies devoted to the investigation
and exploration of knowledge, information, and data processing systems of all kinds, no …

This paper presents the Connectionist Inductive Learning and Logic Programming System
(C-IL 2 P). C-IL 2 P is a new massively parallel computational model based on a feedforward …

Although neural networks have shown very good performance in many application domains,
one of their main drawbacks lies in the incapacity to provide an explanation for the …

There is a long and unresolved debate between the symbolic and sub-symbolic methods.
However, in recent years, there is a push towards in-between methods. In this work, we …

A variety of ideas, approaches, and techniques exist--in terms of both architecture and
learning--and this abundance seems to lead to many exciting possibilities in terms of …