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ORIGINAL RESEARCH article

Front. Robot. AI
Sec. Computational Intelligence in Robotics
Volume 11 - 2024 | doi: 10.3389/frobt.2024.1440631
This article is part of the Research Topic Narrow and General Intelligence: Embodied, Self-Referential Social Cognition and Novelty Production in Humans, AI and Robots View all 6 articles

Machine Psychology: Integrating Operant Conditioning with the Non-Axiomatic Reasoning System for Advancing Artificial General Intelligence Research

Provisionally accepted
Robert Johansson Robert Johansson 1,2*
  • 1 Department of Psychology, Faculty of Social Sciences, Stockholm University, Stockholm, Sweden
  • 2 Department of Computer and Information Science, Faculty of Science and Engineering, Linköping University, Linköping, Östergötland, Sweden

The final, formatted version of the article will be published soon.

    This paper presents an interdisciplinary framework, Machine Psychology, which integrates principles from operant learning psychology with a particular Artifical Intelligence model, the Non-Axiomatic Reasoning System (NARS), to advance Artificial General Intelligence (AGI) research.Central to this framework is the assumption that adaptation is fundamental to both biological and artificial intelligence, and can be understood using operant conditioning principles. The study evaluates this approach through three operant learning tasks using OpenNARS for Applications (ONA): simple discrimination, changing contingencies, and conditional discrimination tasks.In the simple discrimination task, NARS demonstrated rapid learning, achieving 100% correct responses during training and testing phases. The changing contingencies task illustrated NARS's adaptability, as it successfully adjusted its behavior when task conditions were reversed. In the conditional discrimination task, NARS managed complex learning scenarios, achieving high accuracy by forming and utilizing complex hypotheses based on conditional cues.These results validate the use of operant conditioning as a framework for developing adaptive AGI systems. NARS's ability to function under conditions of insufficient knowledge and resources, combined with its sensorimotor reasoning capabilities, positions it as a robust model for AGI.The Machine Psychology framework, by implementing aspects of natural intelligence such as continuous learning and goal-driven behavior, provides a scalable and flexible approach for real-world applications. Future research should explore using enhanced NARS systems, more advanced tasks and applying this framework to diverse, complex tasks to further advance the development of human-level AI.

    Keywords: Artificial General Intelligence (AGI), operant conditioning, Non-Axiomatic Reasoning System (NARS), Machine psychology, Adaptive Learning

    Received: 29 May 2024; Accepted: 30 Jul 2024.

    Copyright: © 2024 Johansson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Robert Johansson, Department of Psychology, Faculty of Social Sciences, Stockholm University, Stockholm, Sweden

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.