Intelligent Agents from Symbolic to Neurosymbolic Systems: The Quest for Integration

In this chapter we take as our reference twenty-five years of scientific and technical results presented at the Workshop on Objects, and explore the development of rational agents and integration with machine learning (ML) techniques, discussing their transition from pure symbolic to subsymbolic and neurosymbolic systems. Given the growing importance of combining rational agent reasoning with ML, we first outline the current state of technology by highlighting key milestones and breakthroughs. Pinpointing logics and logic programming as the main foundational tool for the design and implementation of rational agents, we discuss successful implementations and applications of logic-based agents, then we identify some of the main integration strands of subsymbolic techniques within rational agents. In particular, we focus on symbolic knowledge injection (SKI) and symbolic knowledge extraction (SKE) as some of the most relevant neurosymbolic techniques, and on their impact on intelligent agents and multi-agent systems (MASs). Current gaps and challenges in the integration of rational agents with ML are finally discussed along with future research directions.