Introduction to the Special Issue “Artificial Intelligence Knowledge Representation”

Paola Di Maio,Mari Carmen Suárez-Figueroa

doi:10.3390/systems7030035

Abstract

A guest editorial could have been written when this Special Issue was first announced, to stimulate submissions and guide prospect authors, yet writing it closer to the deadline (together with the deadline extension announcement) makes it possible to highlight recent developments which have happened since [...]

Highlights

The rapid proliferation of Artificial Intelligence (AI) tools, platforms and technologies from academia to industry and beyond, and the consequent media spin, has been worryingly accompanied by the lack of reference to, and poor application of, knowledge representation (KR), despite a wealth of techniques and modelling options. For those who have worked in AI before the current hype cycle, such notable shortfalls may limit the credibility of contributions to AI developments especially in consideration of evolutionary and increasingly autonomous software development techniques, such as neural networks
KR can support the shared and explicit understanding of the socio technical context in which AI systems are deployed, as well can help capture and analyze risks and responsibilities associated with autonomous functions of distributed intelligent systems
Business processes, understanding, explainability, decision making, usability and reliability of intelligent systems all depend on KR

Summary

Introduction

Introduction to the SpecialIssue “Artificial Intelligence Knowledge Representation”Received: 11 July 2019; Accepted: 15 July 2019; Published: 22 July 2019A guest editorial could have been written when this Special Issue was first announced, to stimulate submissions and guide prospect authors, yet writing it closer to the deadline (together with the deadline extension announcement) makes it possible to highlight recent developments which have happened since.The rapid proliferation of Artificial Intelligence (AI) tools, platforms and technologies from academia to industry and beyond, and the consequent media spin, has been worryingly accompanied by the lack of reference to, and poor application of, knowledge representation (KR), despite a wealth of techniques and modelling options.For those who have worked in AI before the current hype cycle, such notable shortfalls may limit the credibility of contributions to AI developments especially in consideration of evolutionary and increasingly autonomous software development techniques, such as neural networks.The lack of adequate application and teaching of KR in AI in large majorities of contemporary AI programs, together with the limited ability for different classes of users to gain insights into AI driven system functions (without having to parse and debug convoluted, encrypted code to test systems behaviour, for example) are important concerns, especially that AI chains drive and underpin system logic at global level, from banking ATMs to personal identity, to online account and workflows of all kinds.KR can provide mechanisms and tools for system logic to be transparent and accountable, necessary qualities for auditability, reliability, and explainability. The rapid proliferation of Artificial Intelligence (AI) tools, platforms and technologies from academia to industry and beyond, and the consequent media spin, has been worryingly accompanied by the lack of reference to, and poor application of, knowledge representation (KR), despite a wealth of techniques and modelling options.

Results

Conclusion