In inductivelogicprogramming (ilp), algorithms which are purely of the bottom-up or top-down type encounter several problems in practice. Since a majority of them axe greedy ones, these algorithms find clauses in lo...
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ISBN:
(纸本)3540229418
In inductivelogicprogramming (ilp), algorithms which are purely of the bottom-up or top-down type encounter several problems in practice. Since a majority of them axe greedy ones, these algorithms find clauses in local optima, according to the "quality" measure used for evaluating the results. Moreover, when learning clauses one by one, induced clauses become less interesting to cover few remaining examples. In this paper, we propose a simulated annealing framework to overcome these problems. Using a refinement operator, we define neighborhood relations on clauses and on hypotheses (i.e. sets of clauses). Withthese relations and appropriate quality measures, we show how to induce clauses (in a coverage approach), or to induce hypotheses directly by using simulated annealing algorithms. We discuss the necessary conditions on the refinement operators and the evaluation measures in order to increase the algorithm's effectivity. Implementations are described and experimentation results are presented.
logic Programs with Annotated Disjunctions (LPADs) provide a simple and elegant framework for integrating probabilistic reasoning and logicprogramming. In this paper we propose an algorithm for learning LPADs. the le...
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ISBN:
(纸本)3540229418
logic Programs with Annotated Disjunctions (LPADs) provide a simple and elegant framework for integrating probabilistic reasoning and logicprogramming. In this paper we propose an algorithm for learning LPADs. the learning problem we consider consists in starting from a sets of interpretations annotated withtheir probability and finding one (or more) LPAD that assign to each interpretation the associated probability. the learning algorithm first finds all the disjunctive clauses that are true in all interpretations, then it assigns to each disjunct in the head a probability and finally decides how to combine the clauses to form an LPAD by solving a constraint satisfaction problem. We show that the learning algorithm is correct and complete.
In this paper, we study how a logical form of scientific modelling that integrates together abduction and induction can be used to understand the functional class of unknown enzymes or inhibitors. We show how we can m...
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ISBN:
(纸本)3540229418
In this paper, we study how a logical form of scientific modelling that integrates together abduction and induction can be used to understand the functional class of unknown enzymes or inhibitors. We show how we can model, within Abductive logicprogramming (ALP), inhibition in metabolic pathways and use abduction to generate facts about inhibition of enzymes by a particular toxin (e.g. Hydrazine) given the underlying metabolic pathway and observations about the concentration of metabolites. these ground facts, together with biochemical background information, can then be generalised by ilp to generate rules about the inhibition by Hydrazine thus enriching further our model. In particular, using Progol 5.0 where the processes of abduction and inductive generalization are integrated enables us to learn such general rules. Experimental results on modelling in this way the effect of Hydrazine in a real metabolic pathway are presented.
there are two types of formalization for induction in logic. In descriptive induction, induced hypotheses describe rules with respect to observations with all predicates minimized. In explanatory induction, on the oth...
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ISBN:
(纸本)3540229418
there are two types of formalization for induction in logic. In descriptive induction, induced hypotheses describe rules with respect to observations with all predicates minimized. In explanatory induction, on the other hand, hypotheses abductively account for observations without any minimization principle. Bothinductive methods have strength and weakness, which are complementary to each other. In this work, we unify these two logical approaches. In the proposed framework, not all predicates are minimized but minimality conditions can be flexibly determined as a circumscription policy. Constructing appropriate policies, we can intentionally minimize models of an augmented axiom set. As a result, induced hypotheses can have both conservativeness and explainability, which have been considered incompatible with each other in the literature. We also give two procedures to compute inductive hypotheses in the proposed framework.
ilp systems induce first-order clausal theories performing a search through very large hypotheses spaces containing redundant hypotheses. the generation of redundant hypotheses may prevent the systems from finding goo...
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inductivelogicprogramming (ilp) is built on a foundation laid by research in machine learning and computational logic. Armed withthis strong foundation, ilp has been applied to important and interesting problems in...
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inductivelogicprogramming (ilp) is built on a foundation laid by research in machine learning and computational logic. Armed withthis strong foundation, ilp has been applied to important and interesting problems in the life sciences, engineering and the arts. this paper begins by briefly reviewing some example applications, in order to illustrate the benefits of ilp. In turn, the applications have brought into focus the need for more research into specific topics. We enumerate and elaborate five of these: (1) novel search methods;(2) incorporation of explicit probabilities;(3) incorporation of special-purpose reasoners;(4) parallel execution using commodity components;and (5) enhanced human interaction. It is our hypothesis that progress in each of these areas can greatly improve the contributions that can be made withilp;and that, with assistance from research workers in other areas, significant progress in each of these areas is possible.
For the last ten years a lot of work has been devoted to propositionalization techniques in relational learning. these techniques change the representation of relational problems to attribute-value problems in order t...
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ISBN:
(纸本)3540229418
For the last ten years a lot of work has been devoted to propositionalization techniques in relational learning. these techniques change the representation of relational problems to attribute-value problems in order to use well-known learning algorithms to solve them. Propositionalization approaches have been successively applied to various problems but are still considered as ad hoc techniques. In this paper, we study these techniques in the larger context of macro-operators as techniques to improve the heuristic search. the macro-operator paradigm enables us to propose a unified view of propositionalization and to discuss its current limitations. We show that a whole new class of approaches can be developed in relational learning which extends the idea of changes of representation to more suited learning languages. As a first step, we propose different languages that provide a better compromise than current propositionalization techniques between the cost of building macro-operators and the cost of learning. It is known that ilp problems can be reformulated either into attribute-value or multi-instance problems. Withthe macro-operator approach, we see that we can target a new representation language we name multi-table. this new language is more expressive than attribute-value but is simpler than multi-instance. Moreover, it is PAC-learnable under weak constraints. Finally, we suggest that relational learning can benefit from boththe problem solving and the attribute-value learning community by focusing on the design of effective macro-operator approaches.
the types of learning systems used in the description language and its related domains are investigated. these learning systems are able to exploit meta-information to improve their performance. An algorithm, which au...
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the types of learning systems used in the description language and its related domains are investigated. these learning systems are able to exploit meta-information to improve their performance. An algorithm, which automatically identifies types from types is proposed. the performance of the algorithm in domains with different characteristics, and its robustness with respect to incomplete observations are studied.
ilp systems have been largely applied to datamining classification tasks with a considerable success. the use of ilp systems in regression tasks has been far less successful. Current systems have very limited numerica...
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ISBN:
(纸本)0769521428
ilp systems have been largely applied to datamining classification tasks with a considerable success. the use of ilp systems in regression tasks has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the application of ilp to discovery of functional relationships of numeric nature. this paper proposes improvements in numerical reasoning capabilities of ilp systems for dealing with regression tasks. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium based on the PAC method to evaluate learning performance. We have found these extensions essential to improve on results over machine learning and statistical-based algorithms used in the empirical evaluation study.
Relatively simple transformations can speed up the execution of queries for data mining considerably. While some ilp systems use such transformations, relatively little is known about them or how they relate to each o...
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Relatively simple transformations can speed up the execution of queries for data mining considerably. While some ilp systems use such transformations, relatively little is known about them or how they relate to each other. this paper describes a number of such transformations. Not all of them are novel, but there have been no studies comparing their efficacy. the main contributions of the paper are: (a) it clarifies the relationship between the transformations;(b) it contains an empirical study of what can be gained by applying the transformations;and (c) it provides some guidance on the kinds of problems that are likely to benefit from the transformations.
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