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Found 7 papers, 2 papers with code
Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCS
Learning Classifier Systems (LCSs) are evolutionary machine learning systems that can be categorised as eXplainable AI (XAI) due to their rule-based nature.
Explainable Artificial Intelligence (XAI)
reinforcement-learning
+1
A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies
Results show the system is able to effectively explore the trade-off between policy performance and complexity, and learn interpretable, high-performing policies that use as few rules as possible.
Lateralization in Agents' Decision Making: Evidence of Benefits/Costs from Artificial Intelligence
Given that lateralization is ubiquitous, it likely has advantages that can benefit artificial intelligence.
Lateralized Learning for Multi-Class Visual Classification Tasks
Hence these systems are not robust against noisy, irrelevant, and redundant data, resulting in the wrong prediction being made with high confidence.
A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems
However, this method was unrefined and suited to only the Multiplexer problem domain.
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
This paper seeks to facilitate the automation of feature transferring in between tasks by utilising the observed list.
Constructing Complexity-efficient Features in XCS with Tree-based Rule Conditions
This paper aims to optimise the structural efficiency of CFs in XOF.
