"Statistical Relational Artificial Intelligence" - seminari formativi - Relatore Prof. Fabrizio Riguzzi dell'Università di Ferrara

15 Ottobre 2018
17 e 18 ottobre 2018, presso la sede di viale Pindaro a Pescara

Il 17 e 18 ottobre 2018, presso la sede di viale Pindaro a Pescara, si svolgeranno due seminari su tematiche relative alla Statistical Relational Artificial Intelligence.
Il relatore sarà il Prof. Fabrizio Riguzzi dell'Università di Ferrara.

  • mercoledì 17 ottobre 2018 ore 16-18 (Aula 20) - Title: Probabilistic Logic Languages
    Abstract: The combination of logic and probability is very useful for modeling domains with complex and uncertain relationships among entities. Many probabilistic logic languages have been proposed in various research fields. In logic programming, the distribution semantics has recently gained an increased attention and is adopted by many languages such as the Independent Choice Logic, PRISM, Logic Programs with Annotated Disjunctions and ProbLog. Other languages instead follow a knowledge-based model construction approach in which the probabilistic logic theory is used directly as a template for generating an underlying complex graphical model. The talk will illustrate these approaches for combining logic and probability and will highlight similarity and differences. The talk will also introduce the types of reasoning that can be performed with these languages: inference, weight learning and structure learning. In inference we want to compute the probability of a query given the model and, possibly, some evidence. In weight learning we know the structural part of the model (the logic formulas) but not the numeric part (the weights) and we want to infer the weights from data. In structure learning we want to infer both the structure and the weights of the model from data.
  • giovedì 18 ottobre 2018 ore 9-11 (Aula Informatica) - Title: Reasoning with Probabilistic Logic Programming Languages
    Abstract: The talk will survey existing approaches for inference and learning in Probabilistic Logic Programming. It will discuss in details algorithms for performing inference on Probabilistic Logic Programming languages that follow the distribution semantics and in particular the PITA algorithm that uses tabling and answer subsumption. The talk will then concentrate on algorithms for learning models following the distribution semantics, discussing first parameter learning and then structure learning. Existing systems for parameter learning use either gradient descent or the EM algorithm. The talk will present various systems for parameter learning of probabilistic logic programs, focusing especially on EMBLEM, that uses EM. Recently, structure learning systems have started to appear, with promising initial results. Various search strategies have been investigated in Probabilistic Inductive Logic Programming. The system SLIPCOVER uses clause revision followed by greedy theory search. All the presented systems are available in the web application http://cplint.eu. Speaker: Fabrizio Riguzzi Fabrizio Riguzzi is Associate Professor of Computer Science at the Department of Mathematics and Computer Science of the University of Ferrara. He was previously Assistant Professor at the same university. He got his Master and PhD in Computer Engineering from the University of Bologna. Fabrizio Riguzzi is vice-president of the Italian Association for Artificial Intelligence and Editor in Chief of Intelligenza Artificiale, the official journal of the Association. He is the author of more than 150 peer reviewed papers in the areas of Machine Learning, Inductive Logic Programming and Statistical Relational Learning. His aim is to develop intelligent systems by combining in novel ways techniques from artificial intelligence, logic and statistics.

    Per informazioni contattare il Prof. Fabio Fioravanti