2D object reconstruction with ASP

Damages to cultural heritage due to human malicious actions or to natural disasters (e.g., earthquakes, tornadoes) are nowadays more and more frequent. Huge work is needed by professional restores to reproduce, as best as possible, the original artwork or architecture opera starting from the potsherds. The tool we are presenting in this paper is devised for being a digital support for this kind of work. As soon as the fragments of the opera are cataloged, a user (possibly young students, and even children, using a tablet or a smartphone as playing with a video game) can propose a partial reconstruction. The final part of the job is left to an ASP program that first computes a pre-processing task to find coherence between (sides of) fragments, and then tries to reconstruct the original object. Experiments are made here focusing on 2D reconstruction (frescoes, reliefs, etc)

An xAI Approach for Data-to-Text Processing with ASP

The generation of natural language text from data series gained renewed interest among AI research goals. Not surprisingly, the few proposals in the state of the art are based on training some system, in order to produce a text that describes and that is coherent to the data provided as input. Main challenges of such approaches are the proper identification of what to say (the key descriptive elements to be addressed in the data) and how to say: the correspondence and accuracy between data and text, the presence of contradictions/redundancy in the text, the control of the amount of synthesis. This paper presents a framework that is compliant with xAI requirements. In particular we model ASP/Python programs that enable an explicit control of accuracy errors and amount of synthesis, with proven optimal solutions. The text description is hierarchically organized, in a top-down structure where text is enriched with further details, according to logic rules. The generation of natural language descriptions’ structure is also managed by logic rules

Students' Careers and AI: a decision-making support system for Academia

In the peculiar realm of higher education, some of the challenges of Public Administration, in terms of quality assurance and data intelligence, can be addressed thanks to the complex ecosystem based on the careers of students and their engagement with the host academia. University governance, ranging from the university Rector and Quality Assurance committee to single heads of degree courses, needs to rely on quantitative and unbiased measures when designing and planning actions. This paper reports on an ongoing project started at Parma University in 2019, that has multiple goals: (1) to collect various sources of students' career-related raw data and to and provide simple access to aggregated analyses through a web portal; (2) to offer an AI based synthesis, in form of automatically generated reports in natural language; (3) to analyze data to detect and predict potential issues (e.g., students drop-out, classes attendance, graduation time estimations, blockages in the career) that can be promptly highlighted, for immediate intervention. As opposed to the majority of academic analytics implementations, particular care is devoted to minimizing ethics and privacy issues and adhering to explainable AI principles in the generation of synthetic explanations of charts and reports. The results of lines of research (2) and (3) will be integrated in the portal (1) that is currently deployed at Parma University

An asp approach for arteries classification in CT-scans

Automated segmentation of CT scans is the first step in the pipeline for the interpretation and identification of potential patholo- gies in human organs. Several methods based on Machine Learning are currently available, even if their precision is still outperformed by med- ical doctors. In this field there are some intrinsic limitations to ML ap- proaches, such as the cost and time to acquire high quality annotated scans for training; a considerably high variability of organs morphol- ogy due to age, health conditions, genetics; acquisition noise. This pa- per outlines a new methodology based on Answer Set Programming, which returns reliable, easy-to-program and explainable interpretations. In particular, we focus on the CT scan analysis and retrieval of tree-like structure, corresponding to main blood vessels (arteries) arrangement. The structure is compared to the knowledge base of vessels contained in anatomy text-books. The mapping of vessels names is computed by an ASP program. This preliminary step produces a robust input to a reasoner for the multi-organ labeling and localization problem

25 Years of Applications of Logic Programming in Italy

We present a review of practical applications of Logic Programming appeared in Italy since 1985. We classify them according to their area of application and discuss some trends emerged in the latest developments. Notwithstanding this survey is far to be comprehensive, it shows that Logic Programming successfully evolved and quickly adapted to new challenges offered by a notable variety of application areas

An asp approach for arteries classification in CT-scans?

Automated segmentation of CT scans is the first step in the pipeline for the interpretation and identification of potential patholo- gies in human organs. Several methods based on Machine Learning are currently available, even if their precision is still outperformed by med- ical doctors. In this field there are some intrinsic limitations to ML ap- proaches, such as the cost and time to acquire high quality annotated scans for training; a considerably high variability of organs morphol- ogy due to age, health conditions, genetics; acquisition noise. This pa- per outlines a new methodology based on Answer Set Programming, which returns reliable, easy-to-program and explainable interpretations. In particular, we focus on the CT scan analysis and retrieval of tree-like structure, corresponding to main blood vessels (arteries) arrangement. The structure is compared to the knowledge base of vessels contained in anatomy text-books. The mapping of vessels names is computed by an ASP program. This preliminary step produces a robust input to a reasoner for the multi-organ labeling and localization problem

An Optimal Data Structure to Handle Dynamic Environment in Non-deterministic Computations.

The single most serious issue in the development of a parallel implementation of non-deterministic programming languages and systems (e.g., logic programming, constraint programming, search-based arti0cial intelligence systems) is the dynamic management of the binding environments—i.e., the ability to associate with each parallel computation the correct set of bindings=values representing the solution generated by that particular branch of the non-deterministic computation. The problem has been abstracted and formally studied previously (ACM Trans. Program. Lang. Syst. 15(4) (1993) 659; New Generation Comput. 17(3) (1999) 285), but to date only relatively ine:cient data structures (ACM Trans. Program. Lang. Syst. (2002); New Generation Comput. 17(3) (1999) 285; J. Funct. Logic Program. Special issue #1 (1999)) have been developed to solve it. We provide a very e:cient solution to the problem (O(lg n) per operation). This is a signi0cant improvement over previously best known ( 3 √ n) solution. Our solution is provably optimal for the pointer machine model. We also show how the solution can be extended to handle the abstraction of search problems in object-oriented systems, with the same time complexity