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1

Grasp and Path-Relinking for Coalition Structure Generation

Di Mauro N., Basile T.M.A., Ferilli S., Esposito F.

Fundamenta Informaticae

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2014

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Vol. 129, nr 3

251--277

EN

In Artificial Intelligence with Coalition Structure Generation (CSG) one refers to those cooperative complex problems that require to find an optimal partition (maximizing a social welfare) of a set of entities involved in a system. The solution of the CSG problem finds applications in many fields such as Machine Learning (set covering machines, clustering), Data Mining (decision tree, discretization), Graph Theory, Natural Language Processing (aggregation), Semantic Web (service composition), and Bioinformatics. The problem of finding the optimal coalition structure is NPcomplete. In this paper we present a greedy adaptive search procedure (GRASP) with path-relinking to efficiently search the space of coalition structures. Experiments and comparisons to other algorithms prove the validity of the proposed method in solving this hard combinatorial problem.

2

A Logic Framework for Incremental Learning of Process Models

Ferilli S., Esposito F.

Fundamenta Informaticae

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2013

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Vol. 128, nr 4

413--443

EN

Standardized processes are important for correctly carrying out activities in an organization. Often the procedures they describe are already in operation, and the need is to understand and formalize them in a model that can support their analysis, replication and enforcement. Manually building these models is complex, costly and error-prone. Hence, the interest in automatically learning them from examples of actual procedures. Desirable options are incrementality in learning and adapting the models, and the ability to express triggers and conditions on the tasks that make up the workflow. This paper proposes a framework based on First-Order Logic that solves many shortcomings of previous approaches to this problem in the literature, allowing to deal with complex domains in a powerful and flexible way. Indeed, First-Order Logic provides a single, comprehensive and expressive representation and manipulation environment for supporting all of the above requirements. A purposely devised experimental evaluation confirms the effectiveness and efficiency of the proposed solution.

3

A General Similarity Framework for Horn Clause Logic

Ferilli S., Basile T.M.A., Biba M., Di Mauro N., Esposito F.

Fundamenta Informaticae

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2009

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Vol. 90, nr 1-2

43-66

EN

First-Order Logic formulć are a powerful representation formalism characterized by the use of relations, that cause serious computational problems due to the phenomenon of indeterminacy (various portions of one description are possibly mapped in different ways onto another description). Being able to identify the correct corresponding parts of two descriptions would help to tackle the problem: hence, the need for a framework for the comparison and similarity assessment. This could have many applications in Artificial Intelligence: guiding subsumption procedures and theory revision systems, implementing flexible matching, supporting instance-based learning and conceptual clustering. Unfortunately, few works on this subject are available in the literature. This paper focuses on Horn clauses, which are the basis for the Logic Programming paradigm, and proposes a novel similarity formula and evaluation criteria for identifying the descriptions components that are more similar and hence more likely to correspond to each other, based only on their syntactic structure. Experiments on real-world datasets prove the effectiveness of the proposal, and the efficiency of the corresponding implementation in the above tasks.

4

Multi-Dimensional Relational Sequence Mining

Esposito F., Di Mauro N., Basile T.M.A., Ferilli S.

Fundamenta Informaticae

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2008

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Vol. 89, nr 1

23-43

EN

The issue addressed in this paper concerns the discovery of frequent multi-dimensional patterns from relational sequences. The great variety of applications of sequential pattern mining, such as user profiling, medicine, local weather forecast and bioinformatics, makes this problem one of the central topics in data mining. Nevertheless, sequential information may concern data on multiple dimensions and, hence, the mining of sequential patterns from multi-dimensional information results very important. In a multi-dimensional sequence each event depends on more than one dimension, such as in spatio-temporal sequences where an event may be spatially or temporally related to other events. In literature, the multi-relational data mining approach has been successfully applied to knowledge discovery fromcomplex data. However, there exists no contribution to manage the general case of multi-dimensional data in which, for example, spatial and temporal information may co-exist. This work takes into account the possibility to mine complex patterns, expressed in a first-order language, in which events may occur along different dimensions. Specifically, multidimensional patterns are defined as a set of atomic first-order formulae in which events are explicitly represented by a variable and the relations between events are represented by a set of dimensional predicates. A complete framework and an Inductive Logic Programming algorithm to tackle this problem are presented along with some experiments on artificial and real multi-dimensional sequences proving its effectiveness.

5

Multistrategy Operators for Relational Learning and Their Cooperation

Esposito F., Fanizzi N., Ferilli S., Basile T.M.A., Di Mauro N.

Fundamenta Informaticae

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2006

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Vol. 69, nr 4

389-409

EN

Traditional Machine Learning approaches based on single inference mechanisms have reached their limits. This causes the need for a framework that integrates approaches based on abduction and abstraction capabilities in the inductive learning paradigm, in the light of Michalski's Inferential Theory of Learning (ITL). This work is intended as a survey of the most significant contributions that are present in the literature, concerning single reasoning strategies and practical ways for bringing them together and making them cooperate in order to improve the effectiveness and efficiency of the learning process. The elicited role of an abductive proof procedure is tackling the problem of incomplete relevance in the incoming examples. Moreover, the employment of abstraction operators based on (direct and inverse) resolution to reduce the complexity of the learning problem is discussed. Lastly, a case study that implements the combined framework into a real multistrategy learning system is briefly presented.

6

A Generalization Model Based on OI-implication for Ideal Theory Refinement

Esposito F., Fanizzi N., Ferilli S., Semeraro G.

Fundamenta Informaticae

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2001

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Vol. 47, nr 1,2

15-33

EN

A framework for theory refinement is presented pursuing the efficiency and effectiveness of learning regarded as a search process. A refinement operator satisfying these requirements is formally defined as ideal. Past results have demonstrated the impossibility of specifying ideal operators in search spaces where standard generalization models, like logical implication or q-subsumption, are adopted. By assuming the object identity bias over a space defined by a clausal language ordered by logical implication, a novel generalization model, named OI-implication, is derived and we prove that ideal operators can be defined for the resulting search space.