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.     

Parallel Logic Programming for Problem Solving

We present a new model for parallel evaluation of logic programs. This model can exploit the main sources of parallelism that the language of logic expresses: Independent AND parallelism and OR parallelism, together with a secondary source emerging as a consequence of the Independent AND Parallelism: the producer/consumer parallelism. The efficiency is derived from the use of ordered structures for managing the information generated throughout the search process. The model is suitable for evaluating programs with a high degree of non-determinism because it never generates two processes for solving the same subgoal and hence it can exploit the same real parallelism generating a lower number of processes than other models. As an application example, we consider the Job Shop Scheduling problem. We report experimental results showing that logic programs can be designed that exhibit parallelism, and that the use of heuristic information translates into speedup in obtaining answers.     

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.     

Metadata for Managing Grid Resources in Data Mining Applications

The Grid is an infrastructure for resource sharing and coordinated use of those resources in dynamic heterogeneous distributed environments. The effective use of a Grid requires the definition of metadata for managing the heterogeneity of involved resources that include computers, data, network facilities, and software tools provided by different organizations. Metadata management becomes a key issue when complex applications, such as data-intensive simulations and data mining applications, are executed on a Grid. This paper discusses metadata models for heterogeneous resource management in Grid-based data mining applications. In particular, it discusses how resources are represented and managed in the Knowledge Grid, a framework for Grid-enabled distributed data mining. The paper illustrates how XML-based metadata is used to describe data mining tools, data sources, mining models, and execution plans, and how metadata is used for the design and execution of distributed knowledge discovery applications on Grids.     

Model Checking with Strong Fairness

In this paper we present a coherent framework for symbolic model checking of linear-time temporal logic (ltl) properties over finite state reactive systems, taking full fairness constraints into consideration. We use the computational model of a fair discrete system (fds) which takes into account both justice (weak fairness) and compassion (strong fairness). The approach presented here reduces the model-checking problem into the question of whether a given fds is feasible (i.e. has at least one computation). The contribution of the paper is twofold: On the methodological level, it presents a direct self-contained exposition of full ltl symbolic model checking without resorting to reductions to either μ-calculus or ctl. On the technical level, it extends previous methods by dealing with compassion at the algorithmic level instead of either adding it to the specification, or transforming compassion to justice. Finally, we extend ctl^∗ with past operators, and show that the basic symbolic feasibility algorithm presented here, can be used to model check an arbitrary ctl^∗ formula over an fds with full fairness constraints. This research was supported in part by an infra-structure grant from the Israeli Ministry of Science and Art, a grant from the U.S.-Israel Binational Science Foundation, and a gift from Intel.     

基于Web的智能教学系统模型

智能教学系统是当前计算机应用的重要领域之一,文中主要研究在国家知识基础设施(NKI)支撑下的基于Web的智能教学系统模型：NKI-Tutor。NKI-Tutor包括七个模块：学生模型、教学模型、交互模型、NKI知识库、专家模型、出题模块和模拟工具,其主要特点是：(1)教学内容覆盖多个学科知识,且是动态的知识库;(2)交互模块给教师提供可操作的教学策略描述语言,便于教师在系统中设计个性化的教学策略。     

Book Review: Exemplar-Based Knowledge Acquisition,by Ray Bareiss. Academic Press, 1989

Summary Exemplar-Based Knowledge Acquisition is an excellent reference document describing a promising knowledge acquisition tool, the Protos system. Since it is publicly available, Protos provides a testbed for a variety of techniques and issues in machine learning: (1) the integration of similarity-based and explanation-based approaches, (2) the transition from user guidance to autonomy, and (3) the relation between knowledge representation and efficiency. Much work remains to be done to assess Protos' scaleability and to uncover and repair any possible existing complexity problems.     

基于结构的多级逻辑优化

本文提出一个基于结构的多级逻辑优化算法ＭＬＯＢＬＳ，多级组合逻辑网络的优化通过分析名逻辑门的可替代函数，并用简单的替代函数作替代变换完成。算法ＭＬＯＢＬＳ具有良好的逻辑结构重构能力，能得到近似最优的多级逻辑结构。整个优化过程在多级逻辑结构上直接进行，其时／空复杂性较少依赖于多级逻辑结构的基本输入／输出数目。／     

A fast algorithm for cellular logic operations on sequential machines

This paper describes an algorithm to efficiently realize repeated Cellular Logic operations. These Cellular Logic operations include erosions and dilations as well as skeletonization (topology-preserving shrinking) and propagation procedures. In the proposed method only those image pixels which might change in an iteration are processed, based on the changed pixels in the previous iteration. Processing times for solid objects (maximum diameter 40 to 200 pixels, 256 × 256 image) on a 10 Mhz Motorola 68000 system range from 250 ms to 400 ms per iteration. A simpler suboptimal method is also described which is 2 to 3 times slower.     

Knowledge Condition Games

Understanding the flow of knowledge in multi-agent protocols is essential when proving the correctness or security of such protocols. Current logical approaches, often based on model checking, are well suited for modeling knowledge in systems where agents do not act strategically. Things become more complicated in strategic settings. In this paper we show that such situations can be understood as a special type of game – a knowledge condition game – in which a coalition “wins” if it is able to bring about some epistemic condition. This paper summarizes some results relating to these games. Two proofs are presented for the computational complexity of deciding whether a coalition can win a knowledge condition game with and without opponents (Σ₂P-complete and NP-complete respectively). We also consider a variant of knowledge condition games in which agents do not know which strategies are played, and prove that under this assumption, the presence of opponents does not affect the complexity. The decision problem without opponents is still NP-complete, but requires a different proof.Sieuwert van Otterloo thanks the Institute for Logic, Language and Information in Amsterdam for its hospitality during the period that this paper was finalized.     

基于重写逻辑的Web服务事务处理形式化描述

Web服务的事务处理研究越来越活跃,对于Web服务中的长、短事务进行形式化描述与验证是很重要的,但目前还没有成熟的方法．该文提出了一种基于重写逻辑的Web服务事务处理形式化描述方法,采用重写逻辑工具Maude,对于描述Web事务的细胞膜演算,给出一个事务处理的通用框架,采用重写逻辑中的规则描述事务的具体活动,并且引入事务补偿机制刻画长事务的运行;并应用该模型形式化描述文中的Web事务经典例子,得到一个可执行的重写逻辑模型,便于以后采用Maude线性时序逻辑分析器进行形式化分析．     

Multi-Dimensional Modal Logic as a Framework for Spatio-Temporal Reasoning

In this paper we advocate the use of multi-dimensional modal logics as a framework for knowledge representation and, in particular, for representing spatio-temporal information. We construct a two-dimensional logic capable of describing topological relationships that change over time. This logic, called PSTL (Propositional Spatio-Temporal Logic) is the Cartesian product of the well-known temporal logic PTL and the modal logic S4_u, which is the Lewis system S4 augmented with the universal modality. Although it is an open problem whether the full PSTL is decidable, we show that it contains decidable fragments into which various temporal extensions (both point-based and interval based) of the spatial logic RCC-8 can be embedded. We consider known decidability and complexity results that are relevant to computation with multi-dimensional formalisms and discuss possible directions for further research.