Finding Hypotheses from Examples by Computing the Least Generalization of Bottom Clauses

Ito, Kimihito; Yamamoto, Akihiro

doi:10.1007/3-540-49292-5_27

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…Moreover, theoretical issues related to the completeness of this form of reasoning have also been discussed by various authors (Yamamoto, 1997;Ito and Yamamoto, 1998;. Some implemented systems have been developed for combining abduction and induction (Muggleton and Bryant, 2000) and others have recently been proposed (Moyle, 2002;Ray, 2003).…”

Section: Introductionmentioning

confidence: 99%

Application of abductive ILP to learning metabolic network inhibition from temporal data

et al. 2006

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In this paper we use a logic-based representation and a combination of Abduction and Induction to model inhibition in metabolic networks. In general, the integration of abduction and induction is required when the following two conditions hold. Firstly, the given background knowledge is incomplete. Secondly, the problem must require the learning of general rules in the circumstance in which the hypothesis language is disjoint from the observation language. Both these conditions hold in the application considered in this paper. Inhibition is very important from the therapeutic point of view since many substances designed to be used as drugs can have an inhibitory effect on other enzymes. Any system able to predict the inhibitory effect of substances on the metabolic network would therefore be very useful in assessing the potential harmful side-effects of drugs. In modelling the phenomenon of inhibition in metabolic networks, background knowledge is used which describes the network topology and functional classes of inhibitors and enzymes. This background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. The foreground examples are derived from in vivo experiments involving NMR analysis of time-varying metabolite concentrations in Mach Learn (2006) 64:209-230 rat urine following injections of toxins. The model's performance is evaluated on training and test sets randomly generated from a real metabolic network. It is shown that even in the case where the hypotheses are restricted to be ground, the predictive accuracy increases with the number of training examples and in all cases exceeds the default (majority class). Experimental results also suggest that when sufficient training data is provided, non-ground hypotheses show a better predictive accuracy than ground hypotheses. The model is also evaluated in terms of the biological insight that it provides.

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Section: Introductionmentioning

confidence: 99%

Application of abductive ILP to learning metabolic network inhibition from temporal data

et al. 2006

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“…These include ILP systems which use some form of Inverse Entailment (e.g. Ito and Yamamoto 1998;Inoue 2001 andRay et al 2003). As future work, we intend to explore methods for dealing with classification noise by relaxing the requirements of consistency in clauses generated by the QG algorithm.…”

Section: Discussionmentioning

confidence: 99%

QG/GA: a stochastic search for Progol

Muggleton

Tamaddoni‐Nezhad

2007

Mach Learn

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Most search techniques within ILP require the evaluation of a large number of inconsistent clauses. However, acceptable clauses typically need to be consistent, and are only found at the "fringe" of the search space. A search approach is presented, based on a novel algorithm called QG (Quick Generalization). QG carries out a random-restart stochastic bottom-up search which efficiently generates a consistent clause on the fringe of the refinement graph search without needing to explore the graph in detail. We use a Genetic Algorithm (GA) to evolve and re-combine clauses generated by QG. In this QG/GA setting, QG is used to seed a population of clauses processed by the GA. Experiments with QG/GA indicate that this approach can be more efficient than standard refinement-graph searches, while generating similar or better solutions.

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“…The development of these frameworks that realize the cycle of integration of abduction and induction prompted the study of the problem of completeness for finding any hypotheses H that satisfies the basic task of finding a consistent hypothesis H such that T ∪ H |= O for a given theory T, and observations O. Progol was found to be incomplete [66] and several new frameworks of integration of abduction and induction have been proposed such as SOLDR [28], CF-induction [27], and HAIL [58]. In particular, HAIL has demonstrated that one of the main reasons for the incompleteness of Progol is that in its cycle of integration of abduction and induction, it uses a very restricted form of abduction.…”

Section: Abduction In Ilp Learningmentioning

confidence: 99%

Abduction and Argumentation for Explainable Machine Learning: A Position Survey

Kakas,

Michael

2020

Preprint

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This paper presents Abduction and Argumentation as two principled forms for reasoning, and fleshes out the fundamental role that they can play within Machine Learning. It reviews the state-of-the-art work over the past few decades on the link of these two reasoning forms with machine learning work, and from this it elaborates on how the explanationgenerating role of Abduction and Argumentation makes them naturallyfitting mechanisms for the development of Explainable Machine Learning and AI systems. Abduction contributes towards this goal by facilitating learning through the transformation, preparation, and homogenization of data. Argumentation, as a conservative extension of classical deductive reasoning, offers a flexible prediction and coverage mechanism for learning -an associated target language for learned knowledge -that explicitly acknowledges the need to deal, in the context of learning, with uncertain, incomplete and inconsistent data that are incompatible with any classically-represented logical theory.

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Finding Hypotheses from Examples by Computing the Least Generalization of Bottom Clauses

Cited by 9 publications

References 5 publications

Application of abductive ILP to learning metabolic network inhibition from temporal data

Application of abductive ILP to learning metabolic network inhibition from temporal data

QG/GA: a stochastic search for Progol

Abduction and Argumentation for Explainable Machine Learning: A Position Survey

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