Markov logic networks

We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach. Editors: Hendrik Blockeel, David Jensen and Stefan Kramer An erratum to this article is available at .     

数字电路门级并行逻辑模拟

对基于事件驱动的电路门级并行逻辑模拟算法和相应的电路划分算法进行了研究。在保守协议的基础上,模拟算法采用流水线技术避免了死锁;采用事件打包,消息队列和非阻塞通讯技术减少了消息传递开销。在聚集分解的基础上,电路划分算法对组合或时序电路都可进行非循环划分,保证流水线模拟不会出现死锁。在曙光集群上采用MPI实现了模拟算法,对ISCAS部分电路进行实验,获得了很好的加速比。最后提出采用预模拟方法的电路划分改进方案。     

System structure for parallel logic programming

A system structure supporting parallel processing in general and parallel logic programming and expert system applications in particular is described. It is not based on special hardware but has rather been designed as an evolutionary extension to most existing machine architectures. It is aimed at parallel processing support for e.g. PROLOG as well as for expert system (shells) implemented in a general purpose language. A layered structure consisting of an extended machine interface and a macro language is chosen to support a range of various applications.     

A parallel execution model of logic programs

A parallel-execution model that can concurrently exploit AND and OR parallelism in logic programs is presented. This model employs a combination of techniques in an approach to executing logic problems in parallel, making tradeoffs among number of processes, degree of parallelism, and combination bandwidth. For interpreting a nondeterministic logic program, this model (1) performs frame inheritance for newly created goals, (2) creates data-dependency graphs (DDGs) that represent relationships among the goals, and (3) constructs appropriate process structures based on the DDGs. (1) The use of frame inheritance serves to increase modularity. In contrast to most previous parallel models that have a large single process structure, frame inheritance facilitates the dynamic construction of multiple independent process structures, and thus permits further manipulation of each process structure. (2) The dynamic determination of data dependency serves to reduce computational complexity. In comparison to models that exploit brute-force parallelism and models that have fixed execution sequences, this model can reduce the number of unification and/or merging steps substantially. In comparison to models that exploit only AND parallelism, this model can selectively exploit demand-driven computation, according to the binding of the query and optional annotations. (3) The construction of appropriate process structures serves to reduce communication complexity. Unlike other methods that map DDGs directly onto process structures, this model can significantly reduce the number of data sent to a process and/or the number of communication channels connected to a process     

An experiment for truly parallel logic programming

A truly parallel logic programming system is proposed. The system is based on the commercially available parallel logic programming language STRAND, which has been extended in order to overcome the inherent limitations of such systems, like AND-type of parallelism, lack of backtracking, limited unification, etc. The system has been tested using an example from the area of natural language processing.     

Markov逻辑网在链接预测中的应用*

链接预测是对实体间的关系进行预测,是一个重要而复杂的任务。传统同类独立同概率分布的方法会带来很大的噪音,导致预测效果很差。将Markov逻辑网应用到链接预测中,旨在改善这一问题。Markov逻辑网是将Markov网与一阶逻辑结合的统计关系学习方法。利用Markov逻辑网构建关系模型,对实体之间是否存在链接关系以及当链接关系存在时预测此链接关系的类型。针对两个数据集的实验结果显示了采用Markov逻辑网模型要比传统链接预测模型有更好的效果,进而为Markov逻辑网解决实际问题提供了依据。     

A parallel logic system on a multicomputer architecture

This paper describes the implementation of a logic programming language on a massively parallel architecture. This implementation is based on the AND/OR Process Model which allows the exploitation of both AND and OR parallelism in logic programs. A distributed memory model is used, and a decentralized control mechanism has been designed. The multicomputer, which the system has been implemented on, consists of a network of Inmos Transputers. The AND/OR processes are implemented as Occam processes mapped onto the Transputer nodes. After the presentation of the system architecture and a deep discussion of the distributed memory management, some preliminary performance results are discussed.     

Compile-time granularity analysis for parallel logic programming languages

The paper describes a simple compiler analysis method for determining the “weight” of procedures in parallel logic programming languages. Using Flat Guarded Horn Clauses (FGHC) as an example, the analysis algorithm is described. Consideration of weights has been incorporated in the scheduler of a real-parallel FGHC emulator running on the Sequent Symmetry multiprocessor. Alternative demand-distribution methods are discussed, includingoldest-first andheaviest-first distributions. Performance measurements, collected from a group of non-trivial benchmarks on eight processors, show that the new schemes donot perform significantly faster than conventional distribution methods. This result is attributed to a combination of factors overshadowing the benefits of the new method: high system overheads, the low cost of spawning a goal on a shared memory multiprocessor, and the increase in synchronization caused by the new methods. Directions of further research are discussed, indicating where further speedup can be attained.     

Adaptive privacy-preserving visualization using parallel coordinates

Current information visualization techniques assume unrestricted access to data. However, privacy protection is a key issue for a lot of real-world data analyses. Corporate data, medical records, etc. are rich in analytical value but cannot be shared without first going through a transformation step where explicit identifiers are removed and the data is sanitized. Researchers in the field of data mining have proposed different techniques over the years for privacy-preserving data publishing and subsequent mining techniques on such sanitized data. A well-known drawback in these methods is that for even a small guarantee of privacy, the utility of the datasets is greatly reduced. In this paper, we propose an adaptive technique for privacy preservation in parallel coordinates. Based on knowledge about the sensitivity of the data, we compute a clustered representation on the fly, which allows the user to explore the data without breaching privacy. Through the use of screen-space privacy metrics, the technique adapts to the user's screen parameters and interaction. We demonstrate our method in a case study and discuss potential attack scenarios.     

Adaptive logspace reducibility and parallel time

We discuss two notions of functional oracle for logarithmic space-bounded machines, which differ in whether there is only one oracle tape for both the query and the answer or a separate tape for the answer, which can still be read while the next query is already being constructed. The first notion turns out to be basically nonadaptive, behaving like access to an oracle set. The second notion, on the other hand, is adaptive. By imposing appropriate bounds on the number of functional oracle queries made in this computation model, we obtain new characterizations of the NC and AC hierarchies; thus the number of oracle queries can be considered as a measure of parallel time. Using this characterization of parallel classes, we solve open questions of Wilson.A preliminary version of this work was presented at STAGS '91 (Hamburg). The work of the first two authors was partially supported by the ESPRIT II Basic Research Actions Program of the EC under Contract No. 3075 (project ALCOM). The work of the third author was done while visiting the Department of Software of UPC in Barcelona supported by Deutsche Forschungsgemeinschaft (DFG-Forschungsstipendium Je 154/1-1). The final version was partially supported by DFG-SFB 342, subproject A4.     

Evidence aggregation networks for fuzzy logic inference

Fuzzy logic has been applied in many engineering disciplines. The problem of fuzzy logic inference is investigated as a question of aggregation of evidence. A fixed network architecture employing general fuzzy unions and intersections is proposed as a mechanism to implement fuzzy logic inference. It is shown that these networks possess desirable theoretical properties. Networks based on parameterized families of operators (such as Yager's union and intersection) have extra predictable properties and admit a training algorithm which produces sharper inference results than were earlier obtained. Simulation studies corroborate the theoretical properties.     

Continuation-based control in the implementation of parallel logic programs

Continuations are used to define the flow of messages between low level tasks in a parallel logic programming language. A combination of compiler and runtime operations reduces message traffic by up to 50% when success continuations are passed as parameters in messages that start new processes. Continuations are also the key to fast task switching, a critical operation in this fine grain parallel system. Data from sample programs shows the effectiveness of continuations in reducing message traffic and the speed with which task switches are performed on a typical host architecture.Supported by NSF Grant CCR-8707177 and grants from Motorola, Inc, and Hewlett-Packard Corp.     

基于时间偏差的并行逻辑模拟的动态负载平衡

随着大规模集成电路的复杂性日益增加,逻辑模拟开始采用并行离散事件模拟技术。在现有的基于时间偏差协议的并行逻辑模拟系统的基础上,提出了一个动态负载平衡模型,模型能够针对模拟时的负载变化,进行以一组模拟对象为单位的迁移以实现负载平衡。提出模拟推进度的概念,作为对并行逻辑模拟过程中的负载进行准确的衡量标准。     

A parallel approach for theorem proving in prepositional logic

In this paper, the divide-and-conquer strategy and a pipelining discipline are applied to theorem proving in propositional logic. The strategy is itself logically complete and sound. Based on this strategy a parallel proof procedure can be constructed. With a pipelined execution model, we show that the processing time using our parallel approach to solve such an NP-complete problem is of O(mn), where m is the number of clauses and n is the number of distinct Boolean variables in the given formula. The approach is simpler than those using explicit inference rules, since the deductions are performed implicitly by only simple checking and deleting operations on each clause.