Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Q-Learningreinforcement-learningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
NARS vs. Reinforcement learning: ONA vs. Q-Learning
One of the realistic scenarios is taking a sequence of optimal actions to do a task. Reinforcement learning is the most well-known approach to deal with this kind of task in the machine learning community. Finding a suit…
Q-Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)DriftScript: A Domain-Specific Language for Programming Non-Axiomatic Reasoning Agents
Non-Axiomatic Reasoning Systems (NARS) provide a framework for building adaptive agents that operate under insufficient knowledge and resources. However, the standard input language, Narsese, poses a usability barrier: i…
ICE-ID: A Novel Historical Census Data Benchmark Comparing NARS against LLMs, \& a ML Ensemble on Longitudinal Identity Resolution
We introduce ICE-ID, a novel benchmark dataset for historical identity resolution, comprising 220 years (1703-1920) of Icelandic census records. ICE-ID spans multiple generations of longitudinal data, capturing name vari…
BenchmarkingPLN and NARS Often Yield Similar strength $\times$ confidence Given Highly Uncertain Term Probabilities
We provide a comparative analysis of the deduction, induction, and abduction formulas used in Probabilistic Logic Networks (PLN) and the Non-Axiomatic Reasoning System (NARS), two uncertain reasoning frameworks aimed at …
From Natural Language to Executable Narsese: A Neuro-Symbolic Benchmark and Pipeline for Reasoning with NARS
Large language models (LLMs) are highly capable at language generation, but they remain unreliable when reasoning requires explicit symbolic structure, multi-step inference, and interpretable uncertainty. This paper pres…