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。

机器心理学：将操作条件反射与非公理推理系统结合，推进人工智能普适性研究

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

This study explores the concept of functional equivalence within the
framework of the Non-Axiomatic Reasoning System (NARS), specifically through
OpenNARS for Applications (ONA). Functional equivalence allows organisms to
categorize and respond to varied stimuli based on their utility rather than
perceptual similarity, thus enhancing cognitive efficiency and adaptability. In
this study, ONA was modified to allow the derivation of functional equivalence.
This paper provides practical examples of the capability of ONA to apply
learned knowledge across different functional situations, demonstrating its
utility in complex problem-solving and decision-making. An extended example is
included, where training of ONA aimed to learn basic human-like language
abilities, using a systematic procedure in relating spoken words, objects and
written words. The research carried out as part of this study extends the
understanding of functional equivalence in AGI systems, and argues for its
necessity for level of flexibility in learning and adapting necessary for
human-level AGI.

该研究通过 Non-Axiomatic Reasoning System (NARS) 框架中的 OpenNARS for Applications (ONA) 探讨功能等同的概念，通过改进 ONA 来推导功能等同。研究提供了 ONA 在不同功能情境中应用所学知识的实际示例，展示了其在复杂问题解决和决策中的实用性。研究扩展了对人工通用智能系统中功能等同的理解，并认为它对于人类级别的学习和适应所必需的灵活性是必要的。

NARS 中的功能等价性

Functional Equivalence with NARS

In recent years, reinforcement learning (RL) has emerged as a popular
approach for solving sequence-based tasks in machine learning. However, finding
suitable alternatives to RL remains an exciting and innovative research area.
One such alternative that has garnered attention is the Non-Axiomatic Reasoning
System (NARS), which is a general-purpose cognitive reasoning framework. In
this paper, we delve into the potential of NARS as a substitute for RL in
solving sequence-based tasks. To investigate this, we conduct a comparative
analysis of the performance of ONA as an implementation of NARS and
$Q$-Learning in various environments that were created using the Open AI gym.
The environments have different difficulty levels, ranging from simple to
complex. Our results demonstrate that NARS is a promising alternative to RL,
with competitive performance in diverse environments, particularly in
non-deterministic ones.

探讨了非公理推理系统（NARS）作为强化学习（RL）替代品的潜力，并进行了性能对比分析，结果表明 NARS 在不同难度环境中表现竞争力尤其是在非确定性环境中。