Modeling temporal processes via belief networks and Petri nets,with application to expert systems

We show how Bayesian belief networks (BNs) can be used to model common temporal knowledge. Two approaches to their structuring are proposed. The first leads to BNs with nodes representing states of a process and times spent in such states, and with a graphical structure reflecting the conditional independence assumptions of a Markovian process. A second approach leads to BNs whose topology represents a conditional independence structure between event-times. Once required distributional specifications are stored within the nodes of a BN, this becomes a powerful inference machine capable, for example, of reasoning backwards in time. We discuss computational difficulties associated with propagation algorithms necessary to perform these inferences, and the reasons why we chose to adopt Monte Carlo-based propagation algorithms. Two improvements to existing Monte Carlo algorithms are proposed; an enhancement based on the principle of importance sampling, and a combined technique that exploits both forward and Markov sampling. Finally, we consider Petri nets, a very interesting and general representation of temporal knowledge. A combined approach is proposed, in which the user structures temporal knowledge in Petri net formalism. The obtained Petri net is then automatically translated into an equivalent BN for probability propagation. Inferred conclusions may finally be explained with the aid of Petri nets again.     

An implementation of inference in temporal branching time models

Issues concerning the implementation of temporal reasoning (inference) for models based on branching time logic as applied to intelligent decision support systems are considered. The focus is on the construction of a qualitative (interval) and quantitative (metric) branching time model. The inference is reduced to solving the temporal constraint satisfaction problem, and the corresponding procedures (algorithms) are proposed. An example of the practical application of the proposed techniques in a prototype of a real-time intelligent decision support system is described.     

Modeling of process parameter selection with mathematical logic for process planning

Process planning is the systematic determination of detailed methods by which workpieces or parts can be manufactured economically and competitively from initial stages to finished stages. One of the key problems of computer-aided process planning (CAPP), however, is the complexity of process knowledge representation of process planning and the diversity of manufacturing background. Process knowledge representation and inference mechanism of process parameter selection is one of the most important issues in the research on CAPP. A proper methodology for modeling inference mechanism of process parameter selection, hence, is essential for selection of process parameters in process planning. The paper presents an atomic inference engine model of process parameter selection in process planning using mathematical logic. The methodology of modeling the inference mechanism of process parameter selection is proposed with backward chaining of mathematical logic that is a form of goal-directed reasoning. An illustrative case has been analyzed using the proposed approach to demonstrate its potential application in the real manufacturing environment, by combining with a practical application of a hole-making in a industrially relevant workpiece. The outcomes of this work provide a process reasoning mechanism for process parameter selection in process planning and thus alleviate automated process reasoning problems in process planning.     

一种专家系统知识获取时的属性约简算法

知识获取是构造专家系统的“瓶颈”,提供准确的推理知识是进行决策规划的关键。文中运用粗糙集理论,通过粗糙集的约简消除冗余的条件属性,实现对知识库的精简。首先研究知识获取,在阐明知识的层次结构基础上,给出了概念化、形式化、知识库求精三个知识获取过程;然后研究属性约简算法,在研究集合差异度和属性的重要性、约简算法推导过程的基础上,给出了属性约简算法的六个步骤。最后根据属性约简算法及其步骤,对功能点分析法构建软件成本估算专家系统时,组成技术复杂因子的14个因素进行了约简。     

Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion

Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. larity based approximate reasoning, an inference result is Combining the conventional compositional rule of inference with simideduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.     

A reasoning algorithm for high-level fuzzy Petri nets

We introduce an automated procedure for extracting information from knowledge bases that contain fuzzy production rules. The knowledge bases considered here are modeled using the high-level fuzzy Petri nets proposed by the authors in the past. Extensions to the high-level fuzzy Petri net model are given to include the representation of partial sources of information. The case of rules with more than one variable in the consequent is also discussed. A reasoning algorithm based on the high-level fuzzy Petri net model is presented. The algorithm consists of the extraction of a subnet and an evaluation process. In the evaluation process, several fuzzy inference methods can be applied. The proposed algorithm is similar to another procedure suggested by Yager (1983), with advantages concerning the knowledge-base searching when gathering the relevant information to answer a particular kind of query     

贝叶斯网络在战术态势估计中的应用

战场态势估计是指挥决策的基础,如何进行合理的态势估计是当前战场指挥系统中最重要的组成部分;作为一种知识表示和进行概率推理的框架,贝叶斯网络在具有内在不确定性的推理和决策问题中得到了广泛的应用;因果推理是态势估计中的一个重要环节,用贝叶斯网络找出态势假设和事件之间的潜在关系,正是态势估计所需完成的功能;根据态势与事件之间不同的连接关系建立态势估计的贝叶斯网络模型,介绍贝叶斯网络推理算法和步骤,并给出实例仿真;结果表明,将贝叶斯网络用于态势估计,能够进行推理得到完整的战场态势信息,为决策提供依据。     

基于改进多嵌入空间的实时语义数据流推理

将语义数据流处理引擎与知识图谱嵌入表示学习相结合,可以有效提高实时数据流推理查询性能,但是现有的知识表示学习模型更多关注静态知识图谱嵌入,忽略了知识图谱的动态特性,导致难以应用于实时动态语义数据流推理任务。为了使知识表示学习模型适应知识图谱的在线更新并能够应用于语义数据流引擎,建立一种基于改进多嵌入空间的动态知识图谱嵌入模型PUKALE。针对传递闭包等复杂推理场景,提出3种嵌入空间生成算法。为了在进行增量更新时更合理地选择嵌入空间,设计2种嵌入空间选择算法。基于上述算法实现PUKALE模型,并将其嵌入数据流推理引擎CSPARQL-engine中,以实现实时语义数据流推理查询。实验结果表明,与传统的CSPARQL和KALE推理相比,PUKALE模型的推理查询时间分别约降低85%和93%,其在支持动态图谱嵌入的同时能够提升实时语义数据流推理准确率。     

2D C-Tree Spatial Representation for Iconic Image

The 2D string approaches provide a natural way of constructing iconic indexing for images. The 2D C-string representation with an efficient cutting mechanism is more characteristic of spatial knowledge and efficient in the representation of iconic images. However, the computation of object ranks in a 2D C-string might make the inference of spatial reasoning somewhat complicated. This shortcoming is overcome by the 2D C-tree representation. The 2D C-tree not only keeps the comprehensive spatial knowledge in the original 2D C-string, but also the ordered labeled tree is more suitable for spatial reasoning and image retrieval. The spatial knowledge can be derived directly from the inference rules embedded in the characteristic structure of the 2D C-tree representation.     

Robust robot knowledge instantiation for intelligent service robots

Robot knowledge is considered to endow service robots with intelligence. In the real environments, robot knowledge needs to represent dynamically changing world. Despite its advantages for semantic knowledge of service robots, robot knowledge may be instantiated and updated by using imperfect sensing data, such as misidentification of object recognition. In case of using commercially available visual recognition system, incorrect knowledge instances are created and changed frequently due to object misidentification and/or recognition failures. In this work, a robust semantic knowledge handling method under imperfect object recognition is proposed to instantiate and update robot knowledge with logical inference by estimating confidence of the object recognition results. The following properties may be applied to determine misidentifications in logical inference: temporal reasoning to represent relationships between time intervals, statistical reasoning with confidence of object recognition results. To show validity of our proposed method, experimental results are illustrated, where commercial visual recognition system is employed.     

面向常识的时间推理

常识和时间推理是人工智能研究的两个主要课题.Allen等人提出的时间推理缺少时间点、时区和时距的统一表示;过分考虑计算,缺少规则推理;求解算法难以应用于多Agent环境并且没有考虑常识不一致性.该文提出一种时间信息表示网络,分析了约束之间的推导规则,给出了常识时间问题的多Agent合作满足弱路径一致性的求解方法.该文的工作改进了Meiri,Wetprasit和Sattar等人的工作,为时间推理结合常识特性和适应多Agent合作环境提供了可行的方案.     

Building knowledge base management systems

Advanced applications in fields such as CAD, software engineering, real-time process control, corporate repositories and digital libraries require the construction, efficient access and management of large, shared knowledge bases. Such knowledge bases cannot be built using existing tools such as expert system shells, because these do not scale up, nor can they be built in terms of existing database technology, because such technology does not support the rich representational structure and inference mechanisms required for knowledge-based systems. This paper proposes a generic architecture for a knowledge base management system intended for such applications. The architecture assumes an object-oriented knowledge representation language with an assertional sublanguage used to express constraints and rules. It also provides for general-purpose deductive inference and special-purpose temporal reasoning. Results reported in the paper address several knowledge base management issues. For storage management, a new method is proposed for generating a logical schema for a given knowledge base. Query processing algorithms are offered for semantic and physical query optimization, along with an enhanced cost model for query cost estimation. On concurrency control, the paper describes a novel concurrency control policy which takes advantage of knowledge base structure and is shown to outperform two-phase locking for highly structured knowledge bases and update-intensive transactions. Finally, algorithms for compilation and efficient processing of constraints and rules during knowledge base operations are described. The paper describes original results, including novel data structures and algorithms, as well as preliminary performance evaluation data. Based on these results, we conclude that knowledge base management systems which can accommodate large knowledge bases are feasible. Edited by Gunter Schlageter and H.-J. Schek. Received May 19, 1994 / Revised May 26, 1995 / Accepted September 18, 1995     

Geo-spatial data mining in the analysis of a demographic database

Spatial data mining refers to the extraction of knowledge, spatial relationships, or other interesting patterns not explicitly stored in spatial databases. The approaches usually followed in the analysis of geo-spatial data with the aim of knowledge discovery are essentially characterised by the development of new algorithms, which treat the position and extension of objects mainly through the manipulation of their co-ordinates. In this paper a new approach to this process is presented, where geographic identifiers give the positional aspects of geographic data. These identifiers are manipulated using qualitative reasoning principles, which allow for the inference of new spatial relations required for the data mining step of the knowledge discovery process. The analysis of a demographic database, with the proposed principles, enabled the discovery of patterns that are hidden in the explored geo-spatial and demographic data.Acknowledgements Our acknowledgment to NEPS (Núcleo de Estudos da População e Sociedade) of University of Minho, for making the demographic data available.     

Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU

This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. The purely rule-based method has its limitations since it requires explicit knowledge of the details of each domain of ICU, such as cardiac domain hence takes years to build knowledge base. Case-based reasoning uses knowledge in the form of specific cases to solve a new problem, and the solution is based on the similarities between the new problem and the available cases. This paper presents a case-based reasoning and rule-based reasoning based model which can provide clinical decision support for all domains of ICU unlike rule-based inference models which are highly domain knowledge specific. Experiments with real ICU data as well as simulated data clearly demonstrate the efficacy of the proposed method.     

A robust continuous model for evidential reasoning

A 2-D model for evidential reasoning is proposed, in which the belief function of evidence is represented as a belief density function which can be in a continuous or discrete form. A vector form of mutual dependency relationship of the evidence is considered and a dependency propagation theorem is proved. This robust method can resolve the conflicts resulting from either the mutual dependency among evidences or the structural dependency in an inference network due to the evidence combination order. Belief conjunction, belief combination, belief propagation procedures, and AND/OR operations of an inference network based on the proposed 2-D model are all presented, followed by some examples demonstrating the advantages of this method over the conventional methods.     

多重多维模糊推理算法的连续性和逼近性

针对多重、多维模糊推理情形,细致地研究了几类模糊推理算法是否满足连续性和逼近性,并进一步讨论了这几类算法对逼近误差的传播性能。把模糊推理算法看成是一个模糊集合到另一个模糊集合的映射,选用海明距离作为两模糊集的距离度量方法,证明了在模糊假言推理和模糊拒取式推理情形,几类多重多维模糊算法都拥有连续性。当多重多维模糊算法满足还原性时就具有逼近性;该模糊算法都不会放大逼近误差。结果对构建模糊控制系统和模糊专家系统时选用和分析模糊推理算法有一定的指导作用。     

CAUPS系统中的时空推理机制与算法

动态城市规划方案仿真系统中的一个重要问题就是抽象和提取仿真对象之间涉及到的空间和时间逻辑关系,并以此作为仿真过程的基本准则和规范动态推理演算。为了建立CAUPS系统中的时空推理机制,针对城市规划方案的动态仿真过程中需要应用到的基本准则和规范,在传统时空关系描述和推理规范的基础上定义出适用于面向城市规划动态仿真的时空关系。考虑到城市规划过程中存在的非刚体对象,提出面向城市辅助规划系统方案应用结果仿真的时间空间推理规范,该规范能够非常好的支持如植被、水域等非刚体对象,并给出一个适合多Agent系统采用的时间空间推理规范执行解决方案,包括时间推理规范算法和空间推理规范算法。面向城市规划的时间推理规范算法和空间推理规范算法已经被成功应用于CAUPS系统底层的多Agent交互关系调整中。     

CUSTOM: A Calibration Region Recovery Approach for Highly Subsampled Dynamic Parallel Magnetic Resonance Imaging

We propose a recovery approach for highly subsampled dynamic parallel MRI image without auto-calibration signals (ACSs) or prior knowledge of coil sensitivity maps. By exploiting the between-frame redundancy of dynamic parallel MRI data, we first introduce a new low-rank matrix recovery-based model, termed as calibration using spatial–temporal matrix (CUSTOM), for ACSs recovery. The recovered ACSs from data are used for estimating coil sensitivity maps and further dynamic image reconstruction. The proposed non-convex and non-smooth minimization for the CUSTOM step is solved by a proximal alternating linearized minimization method, and we provide its convergence result for this specific minimization problem. Numerical experiments on several highly subsampled test data demonstrate that the proposed overall approach outperforms other state-of-the-art methods for calibrationless dynamic parallel MRI reconstruction.     

基于间断区间的时间本体表示及应用

通过间断区间的时态理论扩展了OWL-Time,用在本体中表达单个事件带间断时区的情况。在本体中定义了间断区间概念以及间断区间的时态关系,并以物流领域运输事件的表示为例阐述了领域中基于间断区间的时间知识在本体中的表示方式。通过定义Jena中的原语、规则以及扩展对推理模型的操作构建了相应的推理机,对推理机进行实验检验,不仅验证了推理机运行的正确性,同时也保证了时间知识在本体和规则中表示的正确性,解决了带间断区间的时态知识在语义网中表示和推理的问题。     

On-demand data assimilation of large-scale spatial temporal systems using sequential Monte Carlo methods

The proliferation of sensors is generating rapidly increasing quantities of data like never before. These extensive amounts of data can provide useful information for more accurate state inference of large-scale spatial temporal systems. Sequential Monte Carlo methods are used to assimilate the observed data from sensors to improve the state estimation of large-scale spatial temporal systems, which highly rely on the available real time observation data. In many scenarios, the real time data are limited in space and time. Therefore, it is important to effectively obtain critical sensor data in real time and then dynamically feed them into the running model. In this paper, we propose the on-demand data assimilation method for large-scale spatial temporal systems, in which we quantify the spatial states using run-time state quantification methods and decide if we need to trigger data assimilation on demand and obtain more relevant real time data when the state uncertainty is high. The effectiveness of the developed framework is evaluated based on large-scale wildfire spread simulations.