Page 201 - Artificial Intelligence for the Internet of Everything

CHAPTER 11

Accessing Validity

of Argumentation of Agents

of the Internet of Everything

Boris Galitsky*, Anna Parnis †
*
Oracle Corporation, Redwood Shores, CA, United States
†
Department of Biology, Technion-Israel Institute of Technology, Haifa, Israel

11.1 INTRODUCTION

One of the key features of the Internet of Everything (IoE) is communication
in a complex system that includes people, robots, and machines. According to
Chambers (2014), IoE connects humans, data, processes, and entities to
enhance business communication and facilitate employment, well-being,
education, and health care between various communities of people. As
billions of people are anticipated to be connected the validity, truthfulness,
and authenticity of the textual messages being delivered have become
essential requirements. To make decisions based, in particular, on textual
messages, the claims and their argumentation need to be validated in a
domain-independent manner. The validation of claims in IoE messages
needs to be done based on argumentation patterns rather than on costly
domain-dependent ontologies, which are hard to scale.
Intentionalorunintentionaluntruthfulclaimsand/ortheirfaultyargumen-
tation can lead to accidents, and machines should be able to recognize such
claims and their arguments as a part of tackling human errors (Galitsky,
2015; Lawless, Mittu, Sofge, & Russell, 2017). Frequently human errors are
associated with extreme emotions, so we aim at detecting and validating both
affective and logical argumentation patterns. Intentional disinformation in a
message can also be associated with a security breach (Munro, 2017).
When domain knowledge is available and formalized, truthfulness of a
claim can be validated directly. However, in most environments such
knowledge is unavailable and other implicit means need to come into play,
such as writing style and writing logic (which are domain independent).

Artificial Intelligence for the Internet of Everything Copyright © 2019 Elsevier Inc.
https://doi.org/10.1016/B978-0-12-817636-8.00011-9 All rights reserved. 187