This paper introduces an interdisciplinary framework called Machine
Psychology, which merges principles from operant learning psychology with a
specific Artificial Intelligence model, the Non-Axiomatic Reasoning System
(NARS), to enhance Artificial General Intelligence (AGI) research. The core
premise of this framework is that adaptation is crucial to both biological and
artificial intelligence and can be understood through operant conditioning
principles. The study assesses this approach via 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 perfect accuracy during both training and testing phases. The
changing contingencies task showcased NARS's adaptability, as it successfully
adjusted its behavior when task conditions were reversed. In the conditional
discrimination task, NARS handled complex learning scenarios effectively,
achieving high accuracy by forming and utilizing intricate hypotheses based on
conditional cues.
These findings support the application of operant conditioning as a framework
for creating adaptive AGI systems. NARS's ability to operate under conditions
of insufficient knowledge and resources, coupled with its sensorimotor
reasoning capabilities, establishes it as a robust model for AGI. The Machine
Psychology framework, by incorporating elements of natural intelligence such as
continuous learning and goal-driven behavior, offers a scalable and flexible
approach for real-world applications. Future research should investigate using
enhanced NARS systems, more advanced tasks, and applying this framework to
diverse, complex challenges to further progress the development of human-level
AI.

这篇论文介绍了一种称为机器心理学的跨学科框架，将操作学习心理学原理与特定的人工智能模型非公理推理系统（NARS）相结合，以增强通用人工智能（AGI）研究。该框架的核心前提是适应对于生物和人工智能同样重要，可以通过操作条件性原理来理解。该研究通过三个操作学习任务使用 OpenNARS for Applications（ONA）来评估这种方法：简单判别、条件改变和条件判别任务。这些研究结果支持操作条件性作为创建自适应 AGI 系统的框架的应用。