From liveness to promptness

Liveness temporal properties state that something “good” eventually happens, e.g., every request is eventually granted. In Linear Temporal Logic (LTL), there is no a priori bound on the “wait time” for an eventuality to be fulfilled. That is, F θ asserts that θ holds eventually, but there is no bound on the time when θ will hold. This is troubling, as designers tend to interpret an eventuality F θ as an abstraction of a bounded eventuality F ^≤k θ, for an unknown k, and satisfaction of a liveness property is often not acceptable unless we can bound its wait time. We introduce here prompt-LTL, an extension of LTL with the prompt-eventually operator F _p . A system S satisfies a prompt-LTL formula φ if there is some bound k on the wait time for all prompt-eventually subformulas of φ in all computations of S. We study various problems related to prompt-LTL, including realizability, model checking, and assume-guarantee model checking, and show that they can be solved by techniques that are quite close to the standard techniques for LTL.     

Information Filtering: Selection Mechanisms in Learning Systems

Knowledge has traditionally been considered to have a beneficial effect on the performance of problem solvers but recent studies indicate that knowledge acquisition is not necessarily a monotonically beneficial process, because additional knowledge sometimes leads to a deterioration in system performance. This paper is concerned with the problem of harmful knowledge: that is, knowledge whose removal would improve a system's performance. In the first part of the paper a unifying framework, called theinformation filtering model, is developed to define the various alternative methods for eliminating such knowledge from a learning system where selection processes, called filters, may be inserted to remove potentially harmful knowledge. These filters are termed selective experience, selective attention, selective acquisition, selective retention, and selective utilization. The framework can be used by developers of learning systems as a guide for selecting an appropriate filter to reduce or eliminate harmful knowledge.In the second part of the paper, the framework is used to identify a suitable filter for solving a problem caused by the acquisition of harmful knowledge in a learning system calledLassy.Lassy is a system that improves the performance of a PROLOG interpreter by utilizing acquired domain specific knowledge in the form of lemmas stating previously proved results. It is shown that the particular kind of problems that arise with this system are best solved using a novel utilization filter that blocks the use of lemmas in attempts to prove subgoals that have a high probability of failing.     

Display Calculi for Logics with Relative Accessibility Relations

We define cut-free display calculi for knowledge logics wherean indiscernibility relation is associated to each set of agents, andwhere agents decide the membership of objects using thisindiscernibility relation. To do so, we first translate the knowledgelogics into polymodal logics axiomatised by primitive axioms and thenuse Kracht's results on properly displayable logics to define thedisplay calculi. Apart from these technical results, we argue thatDisplay Logic is a natural framework to define cut-free calculi for manyother logics with relative accessibility relations.     

The Connectionist Inductive Learning and Logic Programming System

This paper presents the Connectionist Inductive Learning and Logic Programming System (C-IL²P). C-IL²P is a new massively parallel computational model based on a feedforward Artificial Neural Network that integrates inductive learning from examples and background knowledge, with deductive learning from Logic Programming. Starting with the background knowledge represented by a propositional logic program, a translation algorithm is applied generating a neural network that can be trained with examples. The results obtained with this refined network can be explained by extracting a revised logic program from it. Moreover, the neural network computes the stable model of the logic program inserted in it as background knowledge, or learned with the examples, thus functioning as a parallel system for Logic Programming. We have successfully applied C-IL²P to two real-world problems of computational biology, specifically DNA sequence analyses. Comparisons with the results obtained by some of the main neural, symbolic, and hybrid inductive learning systems, using the same domain knowledge, show the effectiveness of C-IL²P.     

From Coalition Logic to STIT

STIT is a logic of agency that has been proposed in the nineties in the domain of philosophy of action. It is the logic of constructions of the form “agent a sees to it that φ”. We believe that STIT theory may contribute to the logical analysis of multiagent systems. To support this claim, in this paper we show that there is a close relationship with more recent logics for multiagent systems. We focus on Pauly's Coalition Logic and the logic of the cstit operator, as described by Horty. After a brief presentation of Coalition Logic and a discrete-time version (including a next operator) of the STIT framework, we introduce a translation from Coalition Logic to the discrete STIT logic, and prove that it is correct.     

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.     

EuLisp in education

We present our experience withEuLisp as a teaching language, focussing on the level of the language which was specifically designed for this purpose (level-0).EuLisp has been used in undergraduate and postgraduate teaching since 1990, in lectures and laboratories, where in many cases it has replaced Scheme or Common Lisp. It has been used extensively in programming courses, parallelism courses, as a vehicle for advanced courses in symbolic computing and programming language design; it has also been used as a platform for final year undergraduate projects. This experience has demonstrated thatEuLisp is well suited to teaching and far reaching in its capabilities: it supports the relevant concepts in a consistent and versatile framework, so that the language serves to facilitate the educational process. The discussion is illustrated with examples, and where appropriate we draw a comparison with the Lisp dialects used previously in these courses.     

Automated Refinement of First-Order Horn-Clause Domain Theories

Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. FORTE uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including prepositional theory refinement, first-order induction, and inverse resolution. FORTE is demonstrated in several domains, including logic programming and qualitative modelling.     

Inference of abduction theories for handling incompleteness in first-order learning

In real-life domains, learning systems often have to deal with various kinds of imperfections in data such as noise, incompleteness and inexactness. This problem seriously affects the knowledge discovery process, specifically in the case of traditional Machine Learning approaches that exploit simple or constrained knowledge representations and are based on single inference mechanisms. Indeed, this limits their capability of discovering fundamental knowledge in those situations. In order to broaden the investigation and the applicability of machine learning schemes in such particular situations, it is necessary to move on to more expressive representations which require more complex inference mechanisms. However, the applicability of such new and complex inference mechanisms, such as abductive reasoning, strongly relies on a deep background knowledge about the specific application domain. This work aims at automatically discovering the meta-knowledge needed to abduction inference strategy to complete the incoming information in order to handle cases of missing knowledge. Floriana Esposito received the Laurea degree in electronic Physics from the University of Bari, Italy, in 1970. Since 1994 is Full Professor of Computer Science at the University of Bari and Dean of the Faculty of Computer Science from 1997 to 2002. She founded and chairs the Laboratory for Knowledge Acquisition and Machine Learning of the Department of Computer Science. Her research activity started in the field of numerical models and statistical pattern recognition. Then her interests moved to the field of Artificial Intelligence and Machine Learning. The current research concerns the logical and algebraic foundations of numerical and symbolic methods in machine learning with the aim of the integration, the computational models of incremental and multistrategy learning, the revision of logical theories, the knowledge discovery in data bases. Application include document classification and understanding, content based document retrieval, map interpretation and Semantic Web. She is author of more than 270 scientific papers and is in the scientific committees of many international scientific Conferences in the field of Artificial Intelligence and Machine Learning. She co-chaired ICML96, MSL98, ECML-PKDD 2003, IEA-AIE 2005, ISMIS 2006. Stefano Ferilli was born in 1972. After receiving his Laurea degree in Information Science in 1996, he got a Ph.D. in Computer Science at the University of Bari in 2001. Since 2002 he is an Assistant Professor at the Department of Computer Science of the University of Bari. His research interests are centered on Logic and Algebraic Foundations of Machine Learning, Inductive Logic Programming, Theory Revision, Multi-Strategy Learning, Knowledge Representation, Electronic Document Processing and Digital Libraries. He participated in various National and European (ESPRIT and IST) projects concerning these topics, and is a (co-)author of more than 80 papers published on National and International journals, books and conferences/workshops proceedings. Teresa M.A. Basile got the Laurea degree in Computer Science at the University of Bari, Italy (2001). In March 2005 she discussed a Ph.D. thesis in Computer Science at the University of Bari titled “A Multistrategy Framework for First-Order Rules Learning.” Since April 2005, she is a research at the Computer Science Department of the University of Bari working on methods and techniques of machine learning for the Semantic Web. Her research interests concern the investigation of symbolic machine learning techniques, in particular of the cooperation of different inferences strategies in an incremental learning framework, and their application to document classification and understanding based on their semantic. She is author of about 40 papers published on National and International journals and conferences/workshops proceedings and was/is involved in various National and European projects. Nicola Di Mauro got the Laurea degree in Computer Science at the University of Bari, Italy. From 2001 he went on making research on machine learning in the Knowledge Acquisition and Machine Learning Laboratory (LACAM) at the Department of Computer Science, University of Bari. In March 2005 he discussed a Ph.D. thesis in Computer Science at the University of Bari titled “First Order Incremental Theory Refinement” which faces the problem of Incremental Learning in ILP. Since January 2005, he is an assistant professor at the Department of Computer Science, University of Bari. His research activities concern Inductive Logic Programming (ILP), Theory Revision and Incremental Learning, Multistrategy Learning, with application to Automatic Document Processing. On such topics HE is author of about 40 scientific papers accepted for presentation and publication on international and national journals and conference proceedings. He took part to the European projects 6th FP IP-507173 VIKEF (Virtual Information and Knowledge Environment Framework) and IST-1999-20882 COLLATE (Collaboratory for Annotation, Indexing and Retrieval of Digitized Historical Archive Materials), and to various national projects co-funded by the Italian Ministry for the University and Scientific Research.