6,611 research outputs found
Neuro-symbolic Rule Learning in Real-world Classification Tasks
Neuro-symbolic rule learning has attracted lots of attention as it offers
better interpretability than pure neural models and scales better than symbolic
rule learning. A recent approach named pix2rule proposes a neural Disjunctive
Normal Form (neural DNF) module to learn symbolic rules with feed-forward
layers. Although proved to be effective in synthetic binary classification,
pix2rule has not been applied to more challenging tasks such as multi-label and
multi-class classifications over real-world data. In this paper, we address
this limitation by extending the neural DNF module to (i) support rule learning
in real-world multi-class and multi-label classification tasks, (ii) enforce
the symbolic property of mutual exclusivity (i.e. predicting exactly one class)
in multi-class classification, and (iii) explore its scalability over large
inputs and outputs. We train a vanilla neural DNF model similar to pix2rule's
neural DNF module for multi-label classification, and we propose a novel
extended model called neural DNF-EO (Exactly One) which enforces mutual
exclusivity in multi-class classification. We evaluate the classification
performance, scalability and interpretability of our neural DNF-based models,
and compare them against pure neural models and a state-of-the-art symbolic
rule learner named FastLAS. We demonstrate that our neural DNF-based models
perform similarly to neural networks, but provide better interpretability by
enabling the extraction of logical rules. Our models also scale well when the
rule search space grows in size, in contrast to FastLAS, which fails to learn
in multi-class classification tasks with 200 classes and in all multi-label
settings.Comment: Accepted at AAAI-MAKE 202
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The management of intelligence-assisted finite element analysis technology
Artificial Intelligence (AI) approaches to Finite Element Analysis (FEA), have had tentative degrees of success over the last few years and some authors have argued that effective FEA can help in the manufacture reliability and safety aspects of engineered artefacts. The author of this paper reviews how such AI techniques have been applied and in this light, the author then uses a Fuzzy Cognitive Mapping (FCM), to develop a framework for the management of intelligence-assisted FEA
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
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