Convex optimization approach to identify fusion for multisensortarget tracking

We consider the problem of identity fusion for a multisensor target tracking system whereby the sensors generate reports on the target identities. Since sensor reports are typically fuzzy, incomplete, or inconsistent, the fusion of such sensor reports becomes a major challenge. In this paper, we introduce a new identity fusion method based on the minimization of inconsistencies among the sensor reports by using a convex quadratic programming formulation. In contrast to Dempster-Shafer's evidential reasoning approach which suffers from exponentially growing complexity, our approach is highly efficient (polynomial time solvable). Moreover, our approach can fuse sensor reports of the form more general than that allowed by the evidential reasoning theory. Simulation results show that our method generates reasonable fusion results which are similar to that obtained via the evidential reasoning theory     

D-S证据理论在探地雷达目标识别中的应用

为解决探地雷达的目标识别问题,提出了一种基于雷达扫描数据、实地探测情况、历史信息和已有水文地质信息,并利用D-S证据理论这一具有解决多数据源不确定信息推理和融合特点的理论对目标进行综合识别的方法.实现了探地雷达目标在不确定条件下获得较高可信度的识别.试验结果验证了该理论在探地雷达目标识别上的有效性和可行性.     

Connection Calculus Theorem Proving with Multiple Built-in Theories

Gradually more applications of automated reasoning are discovered. This development has the consequence that deduction systems need to be increasingly flexible. They should exhibit a behavior appropriate to a given problem. One way to achieve this behavior is the integration of different systems or calculi. This leads to the so-called hybrid reasoning (Stickel, 1985; Frisch, 1991; Baumgartner, 1992; Petermann, 1993a) which describes the integration of a general purpose foreground reasoner with one specialized theory reasoner. The aim of this paper is to go a step further, i.e. to treat the theory reasoner as a hybrid system itself. The framework proposed below is suitable for building multiple theories into theorem provers. Those theories can be given syntactically but also semantically. Here, semantical reasoning is understood as reasoning, or rather computing, under a theory given by a class of models, whereas syntactical reasoning means reasoning under a theory given by first-order axioms. The presented approach is a generalization of previous attempts of combining syntactical reasoning under the empty theory with semantical reasoning (Bürckert, 1994; Baumgartner and Stolzenburg, 1995), of combining different theories given syntactically (Petermann, 1997) or just theory (or hybrid) reasoning. The paper formulates sufficient criteria for the construction of complete calculi which enable reasoning under hybrid theories combined from sub-theories given semantically and those given syntactically and briefly reports experimental work.     

A mean field neural network mechanizing complex causal reasoning problems

A neural model for causal reasoning (also called abduction) is proposed in the open, independent, and incompatibility classes. The reasoning process is characterized in these classes by an explicit energy/target function. Potts spin mean field theory annealing methods are used to derive the mechanics of this model. Application of the model to an actual case in legal reasoning and an experimental-based comparison with a recent proposal reveal its efficiency and swiftness in computing the best solutions.     

A method for managing evidential reasoning in a hierarchical hypothesis space

Although informal models of evidential reasoning have been successfully applied in automated reasoning systems, it is generally difficult to define the range of their applicability. In addition, they have not provided a basis for consistent management of evidence bearing on hypotheses that are related hierarchically. The Dempster-Shafer (D-S) theory of evidence is appealing because it does suggest a coherent approach for dealing with such relationships. However, the theory's complexity and potential for computational inefficiency have tended to discourage its use in reasoning systems. In this paper we describe the central elements of the D-S theory, basing our exposition on simple examples drawn from the field of medicine. We then demonstrate the relevance of the D-S theory to a familiar expert-system domain, namely the bacterial-organism identification problem that lies at the heart of the mycin system. Finally, we present a new adaptation of the D-S approach that achieves computational efficiency while permitting the management of evidential reasoning within an abstraction hierarchy.     

基于 D-S 证据理论的地质雷达目标识别

D-S证据理论提供了一种解决多数据源不确定信息推理和融合的有效方法。为解决地质雷达目标识别信息的融合问题,采用D-S证据理论方法,先对目标进行雷达扫描,然后对可能的目标类型进行基本概率分配,最后利用D-S组合公式进行融合识别。试验结果验证了该理论在地质雷达目标识别上的有效性和可行性。     

Cooperative management of a net of intelligent surveillance agent sensors

The use of distributed artificial intelligence (DAI) techniques, particularly the multiagent systems theory, in a decentralized architecture, is proposed to manage cooperatively, all sensor tasks in a network of (air) surveillance radars with capabilities for autonomous operation. At the multisensor data fusion (DF) center, the fusion agent will periodically deliver to sensor agents a list with the system‐level tasks that need to be fulfilled. For each system task, indications about its system‐level priority are included (inferred global necessity of fulfilling the task) as well as the performance objectives that are required, expressed in different terms depending on the type of task (sector surveillance, target tracking, target identification, etc.). Periodically, the local manager at each sensor (the sensor agent) will decide on the list of sensor‐level tasks to be executed by its sensor, providing also the sensor‐level priority and performance objectives for each task. The problem of sensor(s)‐to‐task(s) assignment (including decomposition of system‐level tasks into sensor‐level tasks and translation of system‐level performance requirements to sensor‐level performance objectives) is the result of a negotiation process performed among sensor agents, initiated with the information sent to them by the fusion agent. With types of agents, a symbolic bottom‐up fuzzy reasoning process is performed that considers the available fused or local target tracks, surveillance sectors data, and (external) intelligence information. As a result of these reasoning processes, performed at each agent planning level, the priorities of system‐level and sensor‐level tasks will be inferred and applied during the negation process. © 2003 Wiley Periodicals, Inc.     

Reconstructive explanation: A case study in integral calculus

Expert system explanation has been a significant area of research since the inception of expert systems themselves. The traditional approach to explanation involves the generation of a line of explanation (the reasoning presented in the explanation) that is almost exclusively based on the line of reasoning. Recent reports have described a reconstructive approach to explanation in which the line of explanation and the line of reasoning are often quite distinct and largely decoupled. These reports have emphasized the intuitive motivations for such a reconstructive approach. Our article describes an explicit study of the level and type of decoupling between explanation and problem solving. In particular, we present protocol analysis results showing concrete examples of reconstructive explanation within the area of integral calculus. Further, we discuss the implications of these findings on the instruction of integral calculus.     

Reliability measure assignment to sonar for robust target differentiation

This article addresses the use of evidential reasoning and majority voting in multi-sensor decision making for target differentiation using sonar sensors. Classification of target primitives which constitute the basic building blocks of typical surfaces in uncluttered robot environments has been considered. Multiple sonar sensors placed at geographically different sensing sites make decisions about the target type based on their measurement patterns. Their decisions are combined to reach a group decision through Dempster-Shafer evidential reasoning and majority voting. The sensing nodes view the targets at different ranges and angles so that they have different degrees of reliability. Proper accounting for these different reliabilities has the potential to improve decision making compared to simple uniform treatment of the sensors. Consistency problems arising in majority voting are addressed with a view to achieving high classification performance. This is done by introducing preference ordering among the possible target types and assigning reliability measures (which essentially serve as weights) to each decision-making node based on the target range and azimuth estimates it makes and the belief values it assigns to possible target types. The results bring substantial improvement over evidential reasoning and simple majority voting by reducing the target misclassification rate.     

Parameterized Complexity of Theory of Mind Reasoning in Dynamic Epistemic Logic

Theory of mind refers to the human capacity for reasoning about others’ mental states based on observations of their actions and unfolding events. This type of reasoning is notorious in the cognitive science literature for its presumed computational intractability. A possible reason could be that it may involve higher-order thinking (e.g., ‘you believe that I believe that you believe’). To investigate this we formalize theory of mind reasoning as updating of beliefs about beliefs using dynamic epistemic logic, as this formalism allows to parameterize ‘order of thinking.’ We prove that theory of mind reasoning, so formalized, indeed is intractable (specifically, PSPACE-complete). Using parameterized complexity we prove, however, that the ‘order parameter’ is not a source of intractability. We furthermore consider a set of alternative parameters and investigate which of them are sources of intractability. We discuss the implications of these results for the understanding of theory of mind.     

Nonmonotonic logic and temporal projection

Nonmonotonic formal systems have been proposed as an extension to classical first-order logic that will capture the process of human “default reasoning” or “plausible inference” through their inference mechanisms, just as modus ponens provides a model for deductive reasoning. But although the technical properties of these logics have been studied in detail and many examples of human default reasoning have been identified, for the most part these logics have not actually been applied to practical problems to see whether they produce the expected results.We provide axioms for a simple problem in temporal reasoning which has long been identified as a case of default reasoning, thus presumably amenable to representation in nonmonotonic logic. Upon examining the resulting nonmonotonic theories, however, we find that the inferences permitted by the logics are not those we had intended when we wrote the axioms, and in fact are much weaker. This problem is shown to be independent of the logic used; nor does it depend on any particular temporal representation. Upon analyzing the failure we find that the nonmonotonic logics we considered are inherently incapable of representing this kind of default reasoning.The first part of the paper is an expanded version of one that appeared in the 1986 AAAI proceedings. The second part reports on several responses to our result that have appeared since the original paper was published.     

基于统计证据的mass函数和D-S证据理论的多传感器目标识别

mass函数表示对证据的精确信任程度,是信任函数的基本概率分配.文章在阐述Dempster-Shafer(D-S)证据理论和决策方法的基础上,较系统地论述了基于统计证据的mass函数和D-S证据理论的目标识别的数据融合方法,并给出了具体的识别实例.从计算结果可以看出,该方法有利于目标识别的实现,具有较好的实用性.     

高精度RBP-模糊推理复合学习系统

该文提出了高精度RBP-模糊推理复合学习系统.系统主要由基于鲁棒估计的鲁棒BP学习环节和基于混合合成推理的模糊推理环节构成.该学习系统的主要特点是可由鲁棒BP算法和min-max,max-min模糊推理算法简单地实现.最后通过在目标跟踪问题中应用结果,表示了该算法的高精度和鲁棒性.     

数据融合在目标识别中的应用

介绍了数据融合及其一般功能模型、目标识别融合的三种结构层次 ,给出了目标识别融合的一般分类 ,即物理模型算法、参数分类算法、基于认识模型的算法。着重阐述和比较了参数分类算法中的Bayes理论和证据理论这两种不确定推理方法 ,给出了这两种方法的发展状况。列举了利用融合算法进行生物和军事目标识别的实例。     

高精度RBP－模糊推理复合学习系统

该文提出了高精度ＲＢＰ－模糊推理复合学习系统．系统主要由基于鲁棒估计的鲁棒ＢＰ学习环节和基于混合合成推理的模糊推理环节构成．该学习系统的主要特点是可由鲁棒ＢＰ算法和ｍｉｎ－ｍａｘ，ｍａｘ－ｍｉｎ模糊推理算法简单地实现．最后通过在目标跟踪问题中应用结果，表示了该算法的高精度和鲁棒性．     

自动推理中反证法的研究

计算机自动解几何问题,已经有不少研究成果.前推搜索法能够产生可读证明,因此也是应用较多的一种方法.在目前诸多算法中,一般是采用直接证明的方法.在手工证明几何问题的方法中,间接证明也是一种重要的方法,其中反证法是较为有效的方法之一.在计算机自动推理研究中,如何运用反证法,是自动推理中的一个难题,关于这方面的研究成果也少有报道.给出一个算法:根据命题的结论将命题分类,针对不同类型,设计不同的解决方案.有效地实现了反证法在自动推理中的运用.     

REDRAW — a diagrammatic reasoning system for qualitative structural analysis

For preliminary analysis of structures, human engineers often employ diagrams as a visual language to study and to gain intuitive understanding about the behavior of structures. This paper reports a preliminary study and the development of a prototype system for diagrammatic reasoning to better emulate the intuitive visual problem solving techniques of human engineers. Diagrammatic reasoning is a type of reasoning in which the primary means of inference is the direct manipulation and inspection of a diagram. Diagrammatic reasoning is prevalent in human problem-solving behavior, especially for problems involving spatial relationships among physical objects. Our research examines the relationship between diagrammatic reasoning and symbolic reasoning in a computational framework. We have built a system called REDRAW, that emulates the human capability for reasoning with pictures for qualitative analysis of simple frame structures. Diagrammatic representations provide an environment where inferences about the physical results of proposed structural configurations can take place in a more intuitive manner than that possible through purely symbolic representations.     

Multi-sensor recognition of electronic components

The Dempster-Shafer theory and the convex Bayesian theory have recently been proposed as alternatives to the (strict) Bayesian theory in the field of reasoning with uncertainty. These relatively new formalisms claim that missing information in the probabilistic model of a process not necessarily disables uncertainty reasoning. However, this paper shows that this does not apply to processes where the reasoning is part of a decision-making process, such as object recognition. In these cases, a complete probabilistic model is required and can be obtained by estimating missing probabilistic information. An examplary approach towards the estimation of uncertain probabilistic information is described in this paper for a multi-sensor system for recognition of electronic components on printed circuit boards. Received: 21 June 1998 / Accepted: 23 May 2000     

二值命题逻辑系统的T-真度理论（I）

在二值命题逻辑系统中引入了公式的T-真度概念,并讨论其逻辑运算性质。以此为基础定义了公式的T-相似度和T-伪距离,得到了公式到有限理论结论集的T-伪距离的T-真度表示式,为研究二值命题逻辑系统基于T-真度的近似推理问题提供数值化工具。