基于Value双向近似推理的系统决策方法

先提出Vague值间的一种新的相似度,并基于有序加权聚类(OWA)算子给出Vague集间的新的加权相似测度,其中的权重是由决策者根据实际决策需求来确定的,具有一定的弹性;另外通过Vague输入事实与Vague决策规则的前件(或后件)之间的相似匹配度,得到一个简单可行的双向近似推理方法,这可为复杂环境下的系统决策(如工业检测和医疗诊断等领域问题)提供有效的支持工具。     

系统L在非均匀概率空间下命题的真度理论

将经典二值命题逻辑L中公式的真度概念推广到势为2的非均匀概率空间上;当p∈(0,1)时,证明了全体公式的真度值之集在[0,1]中没有孤立点;利用真度定义公式间的p-相似度和伪距离,进而定义了p-逻辑度量空间,证明了该空间没有孤立点,并在此空间中提出了三种不同类型的近似推理模式。     

Locally linear reconstruction for instance-based learning

Instance-based learning (IBL), so called memory-based reasoning (MBR), is a commonly used non-parametric learning algorithm. k-nearest neighbor (k-NN) learning is the most popular realization of IBL. Due to its usability and adaptability, k-NN has been successfully applied to a wide range of applications. However, in practice, one has to set important model parameters only empirically: the number of neighbors (k) and weights to those neighbors. In this paper, we propose structured ways to set these parameters, based on locally linear reconstruction (LLR). We then employed sequential minimal optimization (SMO) for solving quadratic programming step involved in LLR for classification to reduce the computational complexity. Experimental results from 11 classification and eight regression tasks were promising enough to merit further investigation: not only did LLR outperform the conventional weight allocation methods without much additional computational cost, but also LLR was found to be robust to the change of k.     

Verification of evolving software via component substitutability analysis

This paper presents an automated and compositional procedure to solve the substitutability problem in the context of evolving software systems. Our solution contributes two techniques for checking correctness of software upgrades: (1) a technique based on simultaneous use of over-and under-approximations obtained via existential and universal abstractions; (2) a dynamic assume-guarantee reasoning algorithm—previously generated component assumptions are reused and altered on-the-fly to prove or disprove the global safety properties on the updated system. When upgrades are found to be non-substitutable, our solution generates constructive feedback to developers showing how to improve the components. The substitutability approach has been implemented and validated in the ComFoRT reasoning framework, and we report encouraging results on an industrial benchmark. This is an extended version of a paper, Dynamic Component Substitutability Analysis, published in the Proceedings of the Formal Methods 2005 Conference, Lecture Notes in Computer Science, vol. 3582, by the same authors. This research was sponsored by the National Science Foundation under grant nos. CNS-0411152, CCF-0429120, CCR-0121547, and CCR-0098072, the Semiconductor Research Corporation under grant no. TJ-1366, the US Army Research Office under grant no. DAAD19-01-1-0485, the Office of Naval Research under grant no. N00014-01-1-0796, the ICAST project and the Predictable Assembly from Certifiable Components (PACC) initiative at the Software Engineering Institute, Carnegie Mellon University. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the US government or any other entity.     

Incremental application of knowledge to continuously arriving time-oriented data

In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.     

A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.     

基于模糊Petri网的汽车故障诊断仿真研究

本文将Petri网和模糊推理相结合,建立故障诊断的模糊Petri网模型。其中,用FPN表示模糊产生规则,用Petri网的变迁激活规则进行故障诊断推理,从而分析出异常行为过程间的因果关系,推理出故障的原因及其可信度。以汽车故障诊断为例,建立了基于模糊Petri网的诊断模型。通过仿真分析,验证了模型的正确性和算法的有效性。     

板带钢表面质量实时监测体系研究

板带钢市场竞争的日益激烈,传统的检测方法正逐渐退出应用舞台,更稳定高效表的面检测解决方案成为国内外钢铁企业的找寻目标.针对实际应用的需要,从表面缺陷采集系统,缺陷处理和识别系统、质量智能评价与推理系统等3个方面首次系统地阐述了板带钢表面质量实时监测体系,以期推动目前我国板带钢表面缺陷的在线监测研究水平.     

普适计算环境下上下文信息推理的研究

从各种低层上下文信息得到对人们更加有用的高层上下文信息即上下文推理是当前研究的热点.针对该问题,采用描述逻辑,研究基于本体模型的上下文推理方法.首先简要介绍基于本体的上下文模型,该模型增加了对上下文特性的建模,然后分别研究基于本体的推理、基于规则的推理及不一致性验证3种推理方式,借助Jena框架的推理接口实现,推理功能全面,通用性强,基本满足了普适计算系统中上下文推理的需求,最后给出了推理的可用性.     

直觉线性μ-演算中的合成推理

讨论了以基于前缀封闭集合的Heyting代数的直觉解释的线性μ-演算(IμTL)作为描述“假设-保证”的逻辑基础的问题,提出了一个基于IμTL的“假设-保证”规则.该规则比往常应用线性时序逻辑(LTL)作为规范语言的那些规则具有更好的表达能力,扩展了对形如“always ?”等安全性质的“假设-保证”的范围,具备更一般的“假设-保证”推理能力及对循环推理的支持.