14 research outputs found
Logical Reduction of Metarules
International audienceMany forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called derivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times
Learning from interpreting transitions in explainable deep learning for biometrics
Máster Universitario en Métodos Formales en
IngenierĂa InformáticaWith the rapid development of machine learning algorithms, it has been
applied to almost every aspect of tasks, such as natural language processing, marketing
prediction. The usage of machine learning algorithms is also growing in human resources
departments like the hiring pipeline. However, typical machine learning algorithms learn
from the data collected from society, and therefore the model learned may inherently reflect
the current and historical biases, and there are relevant machine learning algorithms that
have been shown to make decisions largely influenced by gender or ethnicity. How to
reason about the bias of decisions made by machine learning algorithms has attracted more
and more attention. Neural structures, such as deep learning ones (the most successful
machine learning based on statistical learning) lack the ability of explaining their decisions.
The domain depicted in this point is just one example in which explanations are needed.
Situations like this are in the origin of explainable AI. It is the domain of interest for this
project. The nature of explanations is rather declarative instead of numerical. The
hypothesis of this project is that declarative approaches to machine learning could be
crucial in explainable A
Explanatory machine learning for sequential human teaching
The topic of comprehensibility of machine-learned theories has recently drawn
increasing attention. Inductive Logic Programming (ILP) uses logic programming
to derive logic theories from small data based on abduction and induction
techniques. Learned theories are represented in the form of rules as
declarative descriptions of obtained knowledge. In earlier work, the authors
provided the first evidence of a measurable increase in human comprehension
based on machine-learned logic rules for simple classification tasks. In a
later study, it was found that the presentation of machine-learned explanations
to humans can produce both beneficial and harmful effects in the context of
game learning. We continue our investigation of comprehensibility by examining
the effects of the ordering of concept presentations on human comprehension. In
this work, we examine the explanatory effects of curriculum order and the
presence of machine-learned explanations for sequential problem-solving. We
show that 1) there exist tasks A and B such that learning A before B has a
better human comprehension with respect to learning B before A and 2) there
exist tasks A and B such that the presence of explanations when learning A
contributes to improved human comprehension when subsequently learning B. We
propose a framework for the effects of sequential teaching on comprehension
based on an existing definition of comprehensibility and provide evidence for
support from data collected in human trials. Empirical results show that
sequential teaching of concepts with increasing complexity a) has a beneficial
effect on human comprehension and b) leads to human re-discovery of
divide-and-conquer problem-solving strategies, and c) studying machine-learned
explanations allows adaptations of human problem-solving strategy with better
performance.Comment: Submitted to the International Joint Conference on Learning &
Reasoning (IJCLR) 202