404 citations · 828 across the 20 of their papers we have counts for
20 papers
Modularity based linkage model for neuroevolution
Yukai Qiao, Marcus Gallagher
Crossover between neural networks is considered disruptive due to the strong functional dependency between connection weights. We propose a modularity-based linkage model at the we…
Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCS
Jordan T. Bishop, Marcus Gallagher, Will N. Browne
Interest in reinforcement learning (RL) has recently surged due to the application of deep learning techniques, but these connectionist approaches are opaque compared with symbolic…
A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies
Jordan T. Bishop, Marcus Gallagher, Will N. Browne
Reinforcement learning (RL) is experiencing a resurgence in research interest, where Learning Classifier Systems (LCSs) have been applied for many years. However, traditional Michi…
Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement Learning
Humphrey Munn, Marcus Gallagher
Modularity has been widely studied as a mechanism to improve the capabilities of neural networks through various techniques such as hand-crafted modular architectures and automatic…
Approximate discounting-free policy evaluation from transient and recurrent states
Vektor Dewanto, Marcus Gallagher
In order to distinguish policies that prescribe good from bad actions in transient states, we need to evaluate the so-called bias of a policy from transient states. However, we obs…
From Zero-Shot Machine Learning to Zero-Day Attack Detection
Mohanad Sarhan, Siamak Layeghy, Marcus Gallagher +1
The standard ML methodology assumes that the test samples are derived from a set of pre-observed classes used in the training phase. Where the model extracts and learns useful patt…