34 research outputs found
Fuzzy Dynamical Genetic Programming in XCSF
A number of representation schemes have been presented for use within
Learning Classifier Systems, ranging from binary encodings to Neural Networks,
and more recently Dynamical Genetic Programming (DGP). This paper presents
results from an investigation into using a fuzzy DGP representation within the
XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic
Networks are used to represent the traditional condition-action production
system rules. It is shown possible to use self-adaptive, open-ended evolution
to design an ensemble of such fuzzy dynamical systems within XCSF to solve
several well-known continuous-valued test problems.Comment: 2 page GECCO 2011 poster pape
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
An Improved Continuous-Action Extended Classifier Systems for Function Approximation
AbstractDue to their structural simplicity and superior generalization capability, Extended Classifier Systems (XCSs) are gaining popularity within the Artificial Intelligence community. In this study an improved XCS with continuous actions is introduced for function approximation purposes. The proposed XCSF uses “prediction zones,” rather than distinct “prediction values,” to enable multi-member match sets that would allow multiple rules to be evaluated per training step. It is shown that this would accelerate the training procedure and reduce the computational cost associated with the training phase. The improved XCSF is also shown to produce more accurate rules than the classical classifier system when it comes to approximating complex nonlinear functions
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System
Achieving at least some level of explainability requires complex analyses for
many machine learning systems, such as common black-box models. We recently
proposed a new rule-based learning system, SupRB, to construct compact,
interpretable and transparent models by utilizing separate optimizers for the
model selection tasks concerning rule discovery and rule set composition.This
allows users to specifically tailor their model structure to fulfil use-case
specific explainability requirements. From an optimization perspective, this
allows us to define clearer goals and we find that -- in contrast to many state
of the art systems -- this allows us to keep rule fitnesses independent. In
this paper we investigate this system's performance thoroughly on a set of
regression problems and compare it against XCSF, a prominent rule-based
learning system. We find the overall results of SupRB's evaluation comparable
to XCSF's while allowing easier control of model structure and showing a
substantially smaller sensitivity to random seeds and data splits. This
increased control can aid in subsequently providing explanations for both
training and final structure of the model.Comment: arXiv admin note: substantial text overlap with arXiv:2202.0167
Optimality-based Analysis of XCSF Compaction in Discrete Reinforcement Learning
Learning classifier systems (LCSs) are population-based predictive systems
that were originally envisioned as agents to act in reinforcement learning (RL)
environments. These systems can suffer from population bloat and so are
amenable to compaction techniques that try to strike a balance between
population size and performance. A well-studied LCS architecture is XCSF, which
in the RL setting acts as a Q-function approximator. We apply XCSF to a
deterministic and stochastic variant of the FrozenLake8x8 environment from
OpenAI Gym, with its performance compared in terms of function approximation
error and policy accuracy to the optimal Q-functions and policies produced by
solving the environments via dynamic programming. We then introduce a novel
compaction algorithm (Greedy Niche Mass Compaction - GNMC) and study its
operation on XCSF's trained populations. Results show that given a suitable
parametrisation, GNMC preserves or even slightly improves function
approximation error while yielding a significant reduction in population size.
Reasonable preservation of policy accuracy also occurs, and we link this metric
to the commonly used steps-to-goal metric in maze-like environments,
illustrating how the metrics are complementary rather than competitive
SupRB: A Supervised Rule-based Learning System for Continuous Problems
We propose the SupRB learning system, a new Pittsburgh-style learning
classifier system (LCS) for supervised learning on multi-dimensional continuous
decision problems. SupRB learns an approximation of a quality function from
examples (consisting of situations, choices and associated qualities) and is
then able to make an optimal choice as well as predict the quality of a choice
in a given situation. One area of application for SupRB is parametrization of
industrial machinery. In this field, acceptance of the recommendations of
machine learning systems is highly reliant on operators' trust. While an
essential and much-researched ingredient for that trust is prediction quality,
it seems that this alone is not enough. At least as important is a
human-understandable explanation of the reasoning behind a recommendation.
While many state-of-the-art methods such as artificial neural networks fall
short of this, LCSs such as SupRB provide human-readable rules that can be
understood very easily. The prevalent LCSs are not directly applicable to this
problem as they lack support for continuous choices. This paper lays the
foundations for SupRB and shows its general applicability on a simplified model
of an additive manufacturing problem.Comment: Submitted to the Genetic and Evolutionary Computation Conference 2020
(GECCO 2020