Cumulative Scoring-Based Induction of Default Theories

Significant research has been conducted in recent years to extend Inductive Logic Programming (ILP) methods to induce a more expressive class of logic programs such as answer set programs. The methods proposed perform an exhaustive search for the correct hypothesis. Thus, they are sound but not scalable to real-life datasets. Lack of scalability and inability to deal with noisy data in real-life datasets restricts their applicability. In contrast, top-down ILP algorithms such as FOIL, can easily guide the search using heuristics and tolerate noise. They also scale up very well, due to the greedy nature of search for best hypothesis. However, in some cases despite having ample positive and negative examples, heuristics fail to direct the search in the correct direction. In this paper, we introduce the FOLD 2.0 algorithm - an enhanced version of our recently developed algorithm called FOLD. Our original FOLD algorithm automates the inductive learning of default theories. The enhancements presented here preserve the greedy nature of hypothesis search during clause specialization. These enhancements also avoid being stuck in local optima - a major pitfall of FOIL-like algorithms. Experiments that we report in this paper, suggest a significant improvement in terms of accuracy and expressiveness of the class of induced hypotheses. To the best of our knowledge, our FOLD 2.0 algorithm is the first heuristic based, scalable, and noise-resilient ILP system to induce answer set programs

Logic Programming approaches for routing fault-free and maximally-parallel Wavelength Routed Optical Networks on Chip (Application paper)

One promising trend in digital system integration consists of boosting on-chip communication performance by means of silicon photonics, thus materializing the so-called Optical Networks-on-Chip (ONoCs). Among them, wavelength routing can be used to route a signal to destination by univocally associating a routing path to the wavelength of the optical carrier. Such wavelengths should be chosen so to minimize interferences among optical channels and to avoid routing faults. As a result, physical parameter selection of such networks requires the solution of complex constrained optimization problems. In previous work, published in the proceedings of the International Conference on Computer-Aided Design, we proposed and solved the problem of computing the maximum parallelism obtainable in the communication between any two endpoints while avoiding misrouting of optical signals. The underlying technology, only quickly mentioned in that paper, is Answer Set Programming (ASP). In this work, we detail the ASP approach we used to solve such problem. Another important design issue is to select the wavelengths of optical carriers such that they are spread across the available spectrum, in order to reduce the likelihood that, due to imperfections in the manufacturing process, unintended routing faults arise. We show how to address such problem in Constraint Logic Programming on Finite Domains (CLP(FD)). This paper is under consideration for possible publication on Theory and Practice of Logic Programming.Comment: Paper presented at the 33nd International Conference on Logic Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1, 2017. 16 pages, LaTeX, 5 figure

Linking data and BPMN processes to achieve executable models

We describe a formally well founded approach to link data and processes conceptually, based on adopting UML class diagrams to represent data, and BPMN to represent the process. The UML class diagram together with a set of additional process variables, called Artifact, form the information model of the process. All activities of the BPMN process refer to such an information model by means of OCL operation contracts. We show that the resulting semantics while abstract is fully executable. We also provide an implementation of the executor.Peer ReviewedPostprint (author's final draft

Extracting Ontologies From Neural Networks

Artificial neural network-based methods have been growing in popularity, being success- fully applied to perform a variety of tasks. As these systems begin to be deployed in domains where it is desired that they have a certain degree of autonomy and respon- sibility, the need to comprehend the reasoning behind their answers is becoming a re- quirement. Though, neural networks are still regarded as black boxes, since their internal representation do not provide any human-understandable explanation for their outputs. A considerable amount of work has been done towards the development of methods to increase the interpretability of neural networks. However, these methods often produce interpretations are too complex and do not have any declarative meaning, leaving the user with the burden of rationalizing them. Recent work has shown that it is possible to establish mappings between a neural network’s internal representations and a set of human-understandable concepts. In this dissertation we propose a method that leverage these mappings to induce an ontology that describes a neural network’s classification process, through logical relations between human-understandable concepts.Métodos com base em redes neuronais artificiais têm ganho cada vez mais popularidade, e têm sido aplicados na resolução das mais variadas tarefas. À medida que estes sistemas são usados em domínios onde se pretende que tenham um determinado grau de auto- nomia e responsabilidade, a necessidade de compreender o raciocínio que os conduz às suas respostas torna-se indispensável. No entanto, as redes neuronais são vistas como caixas negras, dado que as suas representações internas não constituem uma explicação interpretável para os seus resultados. Tem sido realizada uma quantidade considerável de investigação com o objetivo de desenvolver métodos que permitam o aumento da interpretabilidade de redes neuronais. Todavia, estes métodos tendem a produzir inter- pretações complexas e a que não possuem nenhum significado declarativo, deixando o utilizador com a responsabilidade as racionalizar. Uma publicação recente mostrou que é possível estabelecer mapeamentos entre as representações internas de uma rede neuronal e conceitos interpretáveis. Nesta dissertação propomos um método que faz uso destes mapeamentos para induzir uma ontologia que reflete o processo de classificação de uma rede neuronal, através de conceitos compreensiveis relacionados logicamente