68 research outputs found
A Computational Framework for Formalizing Rules and Managing Changes in Normative Systems
No full textLegal texts are typically written in a natural language. However, a legal text that is written in a formal language has the advantage of being subject to automation, at least partially. Such a translation is not easy, and the matter is even more complex because the law changes with time, so if we formalized a legal text that was originally written in natural language, there is a need to keep track of the change. This thesis proposes original developments on these subjects. In order to formalize a legal document, we provide a pipeline for the translation of a legal text from natural to formal language and we apply it to the case of natural resources contracts. In general, adjectives play an important role in a text and they allow to characterize it: for this reason we developed a logical system aimed at reasoning with gradable adjectives. Regarding norm change, we provide an ontology to represent change in a normative system, some basic mechanisms by which an agent may acquire new norms, and a study on the problem of revising a defeasible theory by only changing its facts. Another contribution of this thesis is a general framework for revision that includes the previous points as specific cases
Anti-skid surface
No full textIl brevetto concerne un dispositivo idoneo all'ottimizzazione delle caratteristiche di resistenza allo scivolamento del piano carrabile (stradale o aeroportuale) finalizzato a incrementare la sicurezza di circolazione e ridurre i tempi di arrest
Hybrid Reinforcement (Rebars + Fibers) for elevated slabs
No full textWhen designing Fiber Reinforced Concrete (FRC) structures, one of the basic issues is represented by
the choice of a proper combination of fibers and conventional reinforcement that allows to obtain the
best structural performance with the minimum amount of materials. The combination of rebars and
fibers in the concrete matrix is generally known as Hybrid Reinforced Concrete (HRC). HRC represents
a feasible solution in many structures; among these, slabs are gaining an increasing interest among
practitioners. In fact, slabs are the most widespread structural elements in common practice since they
are typically used to construct industrial floors (slab on grade), foundations (slab on piles) or floors
(elevated slabs).
This paper focuses on the design of FRC elevated slabs by using the most recent design provisions
reported in the fib Model Code 2010. Emphasis will be given at the use of HRC for optimizing the slab
reinforcement. In more detail, the results of a parametric study performed to design the Hybrid
Reinforcement for elevated slabs will be presented and discussed and a procedure for designing the
Hybrid Reinforcement will be proposed and verified by nonlinear finite element analyses
Classification Rules Explain Machine Learning
No full textWe introduce a general model for explainable Artificial Intelligence that identifies an explanation of a Machine Learning method by classification rules. We define a notion of distance between two Machine Learning methods, and provide a method that computes a set of classification rules that, in turn, approximates another black box method to a given extent. We further build upon this method an anytime algorithm that returns the best approximation it can compute within a given interval of time. This anytime method returns the minimum and maximum difference in terms of approximation provided by the algorithm and uses it to determine whether the obtained approximation is acceptable. We then illustrate the results of a few experiments on three different datasets that show certain properties of the approximations that should be considered while modelling such systems. On top of this, we design a methodology for constructing approximations for ML, that we compare to the no-methods approach typically used in current studies on the explainable artificial intelligence topic
Extraction of Defeasible Proofs as Explanations
No full textHoudini is a Defeasible Deontic Logic reasoner that has been recently developed in Java. The algorithm employed in Houdini follows the proof conditions of the logic to conclude propositional and deontic literals, and is an efficient solution that provides the full extension of a theory. This computation is made in a forward-chaining complete way. Effectiveness is a fundamental property of the adopted approach, but we are also interested in providing an explicit reference to the reasoning that is employed to reach a conclusion. This reasoning is a proof that corresponds to an explanation for that conclusion, and such a proof is less natural to identify in a non-monotonic framework like Defeasible Logic than it would be in a classical one. Depending on the formalism and on the algorithm, the process of reconstructing a proof from a derived conclusion can be cumbersome. Intuitively, a proof consists of a support argument in favour of a literal to be concluded. However, it is necessary also to show that this argument is strong enough, either because the are no arguments against it, or because those arguments are weaker than it. In this paper, with a slight modification of the algorithm of Houdini, we show that it is possible to extract a proof for a defeasible literal in polynomial time, and that such a proof results minimal in its depth
Text Analytics Can Predict Contract Fairness, Transparency and Applicability
No full textThere is a growing attention, in the research communities of political economics, onto the potential of text analytics in classifying documents with economic content. This interest extends the data analytics approach that has been the traditional base for economic theory with scientific perspective. To devise a general method for prediction applicability, we identify some phases of a methodology and perform tests on a large well-structured repository of resource contracts containing documents related to resources. The majority of these contracts involve mining resources. In this paper we prove that, by the usage of text analytics measures, we can cluster these documents on three indicators: fairness of the contract content, transparency of the document themselves, and applicability of the clauses of the contract intended to guarantee execution on an international basis. We achieve these results, consistent with a gold-standard test obtained with human experts, using text similarity b ased on the basic notions of bag of words, the index tf-idf, and three distinct cut-off measures
Letter from Cardinal Prefect Bisletti to Hagan
No full textTypescript letter signed Cardinal Prefect Bisletti of the Congregation for Seminars and Universities, Rome, to Hagan, informing him that Monsignor Luca Pasetto, Bishop of Gerra, has been named visitator for the Irish College
An Ontology of Changes in Normative Systems from an Agentive Viewpoint
No full textDefeasible deontic logic has shown to be expressive enough to represent a normative system, and therefore compliance to such a system can be automatically checked by means of classical model checking techniques of logical systems. However, normative systems are not static, as they can be actively changed by the legislator over time, directly, by changing one norm. Moreover norms can change passively, either by effect of the change of another piece of the normative system, or by means of the change of meaning that affects terms employed in the norm. Although some efforts have been carried out by scholars in the field of legal reasoning about norm change, there is a lack of uniformity in the representation of these changes, and this is an issue when we aim at deploying the law as an automated platform: we need to introduce changes as effects in the semantics of derivation in a logical system, when the unified viewpoint admits a unified representation as well. We adopt the logical paradigm of agency and provide a classification of changes from an agentive viewpoint that allows a unified representation within the logical language for agents LegalRuleML
The architecture of a reasoning system for Defeasible Deontic Logic
No full textWe present the architecture of Houdini-2.0, a reasoning system that computes the extension of a defeasible deontic theory given as We present the architecture of Houdini-2.0, a reasoning system that computes the extension of a defeasible deontic theory given as non-monotonic, and it allows us to determine which prescriptive behaviours are in force (obligations, permissions, prohibitions) input, the process of computing the consequences of the rules expressed in the theory itself. The decision process is a sceptical, input, the process of computing the consequences of the rules expressed in the theory itself. The decision process is a sceptical, along with propositional ones. The system is based on pre-existing algorithmic solutions, and it is implemented as an online platform non-monotonic, and it allows us to determine which prescriptive behaviours are in force (obligations, permissions, prohibitions) non-monotonic, and it allows us to determine which prescriptive behaviours are in force (obligations, permissions, prohibitions) to deploy the results of a computation in several use cases, including those that pertain legal domain. along with propositional ones. The system is based on pre-existing algorithmic solutions, and it is implemented as an online platform along with propositional ones. The system is based on pre-existing algorithmic solutions, and it is implemented as an online platform to deploy the results of a computation in several use cases, including those that pertain legal domain
A Comparison of Pre-Processing Pipelines for the Analysis of Resting-State Data in Epilepsy
No full textNoise removal is a critical step to recover the signal of interest from resting-state fMRI data. Several pre-processing pipelines have been proposed mainly based on nuisance regression or independent component analysis. The aim of this work was to compare the ability of different existing cleaning pipelines in removing spurious non-BOLD signals when applied to a dataset of healthy controls and epilepsy patients. Increased tSNR and power spectral density in the resting-state frequency range (0.01-0.1 Hz) were found for all pre-processing pipelines with respect to the minimally pre-processed data. This suggests a positive gain in terms of temporal properties when optimal cleaning procedures are applied to fMRI data
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