Version 1
: Received: 21 November 2022 / Approved: 24 November 2022 / Online: 24 November 2022 (04:09:24 CET)
Version 2
: Received: 1 November 2023 / Approved: 2 November 2023 / Online: 2 November 2023 (04:11:28 CET)
How to cite:
Ekolle, Z.E.; Kohno, R.; Ochiai, H. A Mathematical Theory of Knowledge for Intelligent Agents. Preprints2022, 2022110450. https://doi.org/10.20944/preprints202211.0450.v2
Ekolle, Z.E.; Kohno, R.; Ochiai, H. A Mathematical Theory of Knowledge for Intelligent Agents. Preprints 2022, 2022110450. https://doi.org/10.20944/preprints202211.0450.v2
Ekolle, Z.E.; Kohno, R.; Ochiai, H. A Mathematical Theory of Knowledge for Intelligent Agents. Preprints2022, 2022110450. https://doi.org/10.20944/preprints202211.0450.v2
APA Style
Ekolle, Z.E., Kohno, R., & Ochiai, H. (2023). A Mathematical Theory of Knowledge for Intelligent Agents. Preprints. https://doi.org/10.20944/preprints202211.0450.v2
Chicago/Turabian Style
Ekolle, Z.E., Ryuji Kohno and Hideki Ochiai. 2023 "A Mathematical Theory of Knowledge for Intelligent Agents" Preprints. https://doi.org/10.20944/preprints202211.0450.v2
Abstract
Knowledge is a property that measures the degree of awareness of an agent about a target in an environment. The goal in conventional intelligent and cognitive agent development is to build agents that can be trained to gain knowledge about a target. The definition and operations of this knowledge associated to the agent is not clear, whereas these are required for developing a reliable, scalable and flexible agent. In this paper, we provide a concise theoretical framework for the description and quantification of the knowledge property needed for an efficient design of cognitive and rational intelligent agents. We relate the quantification scheme to the epistemological description of knowledge and present many illustrative examples on the usefulness of the quantification scheme.
Keywords
intelligent agents; cognitive agents; rational agents; epistemology; machine learning; information theory; information geometry; relativity theory; value systems; valuation modeling
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Zie Eya Ekolle
Commenter's Conflict of Interests: Author