Abductive and Consistency-Based Diagnosis Revisited: a Modeling Perspective

Diagnostic reasoning has been characterized logically as consistency-based reasoning or abductive reasoning. Previous analyses in the literature have shown, on the one hand, that choosing the (in general more restrictive) abductive definition may be appropriate or not, depending on the content of the knowledge base [Console&Torasso91], and, on the other hand, that, depending on the choice of the definition the same knowledge should be expressed in different form [Poole94]. Since in Model-Based Diagnosis a major problem is finding the right way of abstracting the behavior of the system to be modeled, this paper discusses the relation between modeling, and in particular abstraction in the model, and the notion of diagnosis.Comment: 5 pages, 8th Int. Workshop on Nonmonotonic Reasoning, 200

Answer Set Programming for Legal Decision Support and Explanation

The ANGELIC methodology was successfully used to predict decisions of the European Court of Human Rights based on a set of logical rules, with significantly better accuracy than the one achieved by machine learning approaches, as well as to explain the results of reasoning, quite valuable in order to make them trustworthy. This work demonstrates a different logic-based approach, based on Answer Set Programming for solving and generating explanations for solutions. The use of a general knowledge representation and reasoning system, where representation and inference are not tightly coupled, allows for using the same representation for inference tasks different from prediction, thus getting more value out of the domain model, and opens for integrating further forms of knowledge

An ASP Approach for Reasoning on Neural Networks under a Finitely Many-Valued Semantics for Weighted Conditional Knowledge Bases

Weighted knowledge bases for description logics with typicality have been recently considered under a "concept-wise" multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of multilayer perceptrons (MLPs). In this paper we consider weighted conditional ALC knowledge bases with typicality in the finitely many-valued case, through three different semantic constructions. For the boolean fragment LC of ALC we exploit answer set programming and asprin for reasoning with the concept-wise multipreference entailment under a phi-coherent semantics, suitable to characterize the stationary states of MLPs. As a proof of concept, we experiment the proposed approach for checking properties of trained MLPs

Weighted conditional E L knowledge bases with integer weights: An ASP approach

Weighted knowledge bases for description logics with typicality have been recently considered under a “concept-wise” multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of Multilayer Perceptrons. In this paper we consider weighted conditional E L knowledge bases in the two-valued case, and exploit ASP and asprin for encoding concept-wise multipreference entailment for weighted KBs with integer weights

A framework for a modular multi-concept lexicographic closure semantics (an abridged report)

We define a modular multi-concept extension of the lexicographic closure semantics for defeasible description logics with typicality. The idea is that of distributing the defeasible properties of concepts into different modules, according to their subject, and of defining a notion of preference for each module based on the lexicographic closure semantics. The preferential semantics of the knowledge base can then be defined as a combination of the preferences of the single modules. The range of possibilities, from fine grained to coarse grained modules, provides a spectrum of alternative semantics

Multilayer Perceptrons as Weighted Conditional Knowledge Bases: An Overview

In this paper we report about the relationships between a multi-preferential semantics for defeasible description logics and a deep neural network model. Weighted knowledge bases for description logics are considered under a “concept-wise" preferential semantics, which is further extended to fuzzy interpretations and exploited to provide a preferential interpretation of Multilayer Perceptrons