Learning hierarchical probabilistic logic programs

Machine Learning - Probabilistic logic programming (PLP) combines logic programs and probabilities. Due to its expressiveness and simplicity, it has been considered as a powerful tool for learning...     

Belief revision via Lamarckian evolution

We present a system for performing belief revision in a multi-agent environment. The system is called GBR (Genetic Belief Revisor) and it is based on a genetic algorithm. In this setting, different individuals are exposed to different experiences. This may happen because the world surrounding an agent changes over time or because we allow agents exploring different parts of the world. The algorithm permits the exchange of chromosomes from different agents and combines two different evolution strategies, one based on Darwin’s and the other on Lamarck’s evolutionary theory. The algorithm therefore includes also a Lamarckian operator that changes the memes of an agent in order to improve their fitness. The operator is implemented by means of a belief revision procedure that, by tracing logical derivations, identifies the memes leading to contradiction. Moreover, the algorithm comprises a special crossover mechanism for memes in which a meme can be acquired from another agent only if the other agent has “accessed” the meme, i.e. if an application of the Lamarckian operator has read or modified the meme. Experiments have been performed on the η-queen problem and on a problem of digital circuit diagnosis. In the case of the η-queen problem, the addition of the Lamarckian operator in the single agent case improves the fitness of the best solution. In both cases the experiments show that the distribution of constraints, even if it may lead to a reduction of the fitness of the best solution, does not produce a significant reduction. Evelina Lamma, Ph.D.: She is Full Professor at the University of Ferrara. She got her degree in Electrical Engineering at the University of Bologna in 1985, and her Ph.D. in Computer Science in 1990. Her research activity centers on extensions of logic programming languages and artificial intelligence. She was coorganizers of the 3rd International Workshop on Extensions of Logic Programming ELP92, held in Bologna in February 1992, and of the 6th Italian Congress on Artificial Intelligence, held in Bologna in September 1999. Currently, she teaches Artificial Intelligence and Fondations of Computer Science. Fabrizio Riguzzi, Ph.D.: He is Assistant Professor at the Department of Engineering of the University of Ferrara, Italy. He received his Laurea from the University of Bologna in 1995 and his Ph.D. from the University of Bologna in 1999. He joined the Department of Engineering of the University of Ferrara in 1999. He has been a visiting researcher at the University of Cyprus and at the New University of Lisbon. His research interests include: data mining (and in particular methods for learning from multirelational data), machine learning, belief revision, genetic algorithms and software engineering. Luís Moniz Pereira, Ph.D.: He is Full Professor of Computer Science at Departamento de Informática, Universidade Nova de Lisboa, Portugal. He received his Ph.D. in Artificial Intelligence from Brunel University in 1974. He is the director of the Artificial Intelligence Centre (CENTRIA) at Universidade Nova de Lisboa. He has been elected Fellow of the European Coordinating Committee for Artificial Intelligence in 2001. He has been a visiting Professor at the U. California at Riverside, USA, the State U. NY at Stony Brook, USA and the U. Bologna, Italy. His research interests include: knowledge representation, reasoning, learning, rational agents and logic programming.     

Applying the information bottleneck to statistical relational learning

In this paper we propose to apply the Information Bottleneck (IB) approach to the sub-class of Statistical Relational Learning (SRL) languages that are reducible to Bayesian networks. When the resulting networks involve hidden variables, learning these languages requires the use of techniques for learning from incomplete data such as the Expectation Maximization (EM) algorithm. Recently, the IB approach was shown to be able to avoid some of the local maxima in which EM can get trapped when learning with hidden variables. Here we present the algorithm Relational Information Bottleneck (RIB) that learns the parameters of SRL languages reducible to Bayesian Networks. In particular, we present the specialization of RIB to a language belonging to the family of languages based on the distribution semantics, Logic Programs with Annotated Disjunction (LPADs). This language is prototypical for such a family and its equivalent Bayesian networks contain hidden variables. RIB is evaluated on the IMDB, Cora and artificial datasets and compared with LeProbLog, EM, Alchemy and PRISM. The experimental results show that RIB has good performances especially when some logical atoms are unobserved. Moreover, it is particularly suitable when learning from interpretations that share the same Herbrand base.     

Strategies in Combined Learning via Logic Programs

We discuss the adoption of a three-valued setting for inductive concept learning. Distinguishing between what is true, what is false and what is unknown can be useful in situations where decisions have to be taken on the basis of scarce, ambiguous, or downright contradictory information. In a three-valued setting, we learn a definition for both the target concept and its opposite, considering positive and negative examples as instances of two disjoint classes. To this purpose, we adopt Extended Logic Programs (ELP) under a Well-Founded Semantics with explicit negation (WFSX) as the representation formalism for learning, and show how ELPs can be used to specify combinations of strategies in a declarative way also coping with contradiction and exceptions.Explicit negation is used to represent the opposite concept, while default negation is used to ensure consistency and to handle exceptions to general rules. Exceptions are represented by examples covered by the definition for a concept that belong to the training set for the opposite concept.Standard Inductive Logic Programming techniques are employed to learn the concept and its opposite. Depending on the adopted technique, we can learn the most general or the least general definition. Thus, four epistemological varieties occur, resulting from the combination of most general and least general solutions for the positive and negative concept. We discuss the factors that should be taken into account when choosing and strategically combining the generality levels for positive and negative concepts.In the paper, we also handle the issue of strategic combination of possibly contradictory learnt definitions of a predicate and its explicit negation.All in all, we show that extended logic programs under well-founded semantics with explicit negation add expressivity to learning tasks, and allow the tackling of a number of representation and strategic issues in a principled way.Our techniques have been implemented and examples run on a state-of-the-art logic programming system with tabling which implements WFSX.     

Probabilistic logic programming on the web

We present the web application ‘cplint on SWI‐Prolog for SHaring that allows the user to write (SWISH)' Probabilistic Logic Programs and submit the computation of the probability of queries with a web browser. The application is based on SWISH, a web framework for Logic Programming. SWISH is based on various features and packages of SWI‐Prolog, in particular, its web server and its Pengine library, that allow to create remote Prolog engines and to pose queries to them. In order to develop the web application, we started from the PITA system, which is included in cplint , a suite of programs for reasoning over Logic Programs with Annotated Disjunctions, by porting PITA to SWI‐Prolog. Moreover, we modified the PITA library so that it can be executed in a multi‐threading environment. Developing ‘cplint on SWISH’ also required modification of the JavaScript SWISH code that creates and queries Pengines. ‘cplint on SWISH’ includes a number of examples that cover a wide range of domains and provide interesting applications of Probabilistic Logic Programming. By providing a web interface to cplint , we allow users to experiment with Probabilistic Logic Programming without the need to install a system, a procedure that is often complex, error prone, and limited mainly to the Linux platform. In this way, we aim to reach out to a wider audience and popularize Probabilistic Logic Programming. Copyright © 2015 John Wiley & Sons, Ltd.     

Probabilistic inductive constraint logic

Machine Learning - Probabilistic logical models deal effectively with uncertain relations and entities typical of many real world domains. In the field of probabilistic logic programming usually...     

Lifted discriminative learning of probabilistic logic programs

Machine Learning - Probabilistic logic programming (PLP) provides a powerful tool for reasoning with uncertain relational models. However, learning probabilistic logic programs is expensive due to...     

Artificial intelligence techniques for monitoring dangerous infections.

The monitoring and detection of nosocomial infections is a very important problem arising in hospitals. A hospital-acquired or nosocomial infection is a disease that develops after admission into the hospital and it is the consequence of a treatment, not necessarily a surgical one, performed by the medical staff. Nosocomial infections are dangerous because they are caused by bacteria which have dangerous (critical) resistance to antibiotics. This problem is very serious all over the world. In Italy, almost 5-8% of the patients admitted into hospitals develop this kind of infection. In order to reduce this figure, policies for controlling infections should be adopted by medical practitioners. In order to support them in this complex task, we have developed a system, called MERCURIO, capable of managing different aspects of the problem. The objectives of this system are the validation of microbiological data and the creation of a real time epidemiological information system. The system is useful for laboratory physicians, because it supports them in the execution of the microbiological analyses; for clinicians, because it supports them in the definition of the prophylaxis, of the most suitable antibi-otic therapy and in monitoring patients' infections; and for epidemiologists, because it allows them to identify outbreaks and to study infection dynamics. In order to achieve these objectives, we have adopted expert system and data mining techniques. We have also integrated a statistical module that monitors the diffusion of nosocomial infections over time in the hospital, and that strictly interacts with the knowledge based module. Data mining techniques have been used for improving the system knowledge base. The knowledge discovery process is not antithetic, but complementary to the one based on manual knowledge elicitation. In order to verify the reliability of the tasks performed by MERCURIO and the usefulness of the knowledge discovery approach, we performed a test based on a dataset of real infection events. In the validation task MERCURIO achieved an accuracy of 98.5%, a sensitivity of 98.5% and a specificity of 99%. In the therapy suggestion task, MERCURIO achieved very high accuracy and specificity as well. The executed test provided many insights to experts, too (we discovered some of their mistakes). The knowledge discovery approach was very effective in validating part of the MERCURIO knowledge base, and also in extending it with new validation rules, confirmed by interviewed microbiologists and specific to the hospital laboratory under consideration.