Modelling and Implementing a Knowledge Base for Checking Medical Invoices with DLV

Checking medical invoices, done by every health insurance company, is a labor-intensive task. Both speed and quality of executing this task may be increased by the knowledge-based decision support system ACMI which we present in this paper. As the relevant regulations also contain various default rules, ACMI`s knowledge core is modelled using the answer set programming paradigm. It turned out that all relevant rules could be expressed directly in this framework, providing for a declarative and easily extendable and modifiable knowledge base. ACMI is implemented using the DLV system

aspBEEF: Explaining Predictions Through Optimal Clustering

[Abstract] In this paper we introduce aspBEEF, a tool for generating explanations for the outcome of an arbitrary machine learning classifier. This is done using Grover’s et al. framework known as Balanced English Explanations of Forecasts (BEEF) that generates explanations in terms of in terms of finite intervals over the values of the input features. Since the problem of obtaining an optimal BEEF explanation has been proved to be NP-complete, BEEF existing implementation computes an approximation. In this work we use instead an encoding into the Answer Set Programming paradigm, specialized in solving NP problems, to guarantee that the computed solutions are optimal.Ministerio de Asuntos Económicos y Transformación Digital; TIN2017-84453-PXunta de Galicia; GPC ED431B 2019/03Xunta de Galicia; ED431G 2019/0

Answer Set Programming Modulo `Space-Time'

We present ASP Modulo `Space-Time', a declarative representational and computational framework to perform commonsense reasoning about regions with both spatial and temporal components. Supported are capabilities for mixed qualitative-quantitative reasoning, consistency checking, and inferring compositions of space-time relations; these capabilities combine and synergise for applications in a range of AI application areas where the processing and interpretation of spatio-temporal data is crucial. The framework and resulting system is the only general KR-based method for declaratively reasoning about the dynamics of `space-time' regions as first-class objects. We present an empirical evaluation (with scalability and robustness results), and include diverse application examples involving interpretation and control tasks

Model Generation for Generalized Quantifiers via Answer Set Programming

For the semantic evaluation of natural language sentences, in particular those containing generalized quantifiers, we subscribe to the generate and test methodology to produce models of such sentences. These models are considered as means by which the sentences can be interpreted within a natural language processing system. The goal of this paper is to demonstrate that answer set programming is a simple, efficient and particularly well suited model generation technique for this purpose, leading to a straightforward implementation