Information and probabilistic reasoning

In this paper, the relationship between information and reasoning is investigated and a parallel reasoning method is proposed based on information theory, in particular the principle of minimum cross entropy. Some technical issues, such as multiple uncertain evidence, complicated constraints, small directed cycles and decomposition of underlying networks, are discussed. Some simple examples are also given to compare the method proposed here with other methods.     

Modelling default and likelihood reasoning as probabilistic reasoning

This paper presents a probabilistic analysis of plausible reasoning about defaults and about likelihood. Likely and by default are in fact treated as duals in the same sense as possibility and necessity. To model these four forms probabilistically, a logicQDP and its quantitative counterpartDP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequence results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying prepositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance.Much of this research was done while at the University of Technology, Sydney, Broadway, NSW, Australia, and some at the Turing Institute, 36 Nth. Hanover Str., Glasgow, Scotland.     

A propositional probabilistic logic with discrete linear time for reasoning about evidence

The aim of the paper is to present a sound, strongly complete and decidable probabilistic temporal logic that can model reasoning about evidence. The formal system developed here is actually a solution of a problem proposed by Halpern and Pucella (J Artif Intell Res 26:1–34, 2006).     

Integrating integer programming and probabilistic deduction graphs for probabilistic reasoning

Optimal solutions of several variants of the probabilistic reasoning problem were found by a new technique that integrates integer programming and probabilistic deduction graphs (PDG). PDGs are extended from deduction graphs of the and-type via normal deduction graphs. The foregoing variants to be solved can involve multiple hypotheses and multiple evidences where the former is given and the latter is unknown and being found or vice versa. The relationship among these hypotheses and evidences with possible intermediaries is represented by a causal graph. The proposed method can handle a large causal graph of any type and find an optimal solution by invoking a linear integer programming package. In addition, formulating the reasoning problem to fit integer programming takes a polynomial time. H.-L. Li was visiting the Department of Computer Sciences, University of North Texas in 1988–1989. He is with the Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.     

Formal reasoning for analyzing goal models that evolve over time

Requirements Engineering - In early-phase requirements engineering, modeling stakeholder goals and intentions helps stakeholders understand the problem context and evaluate tradeoffs, by exploring...     

A visual language for explaining probabilistic reasoning

We present an explanation-oriented, domain-specific, visual language for explaining probabilistic reasoning. Explanation-oriented programming is a new paradigm that shifts the focus of programming from the computation of results to explanations of how those results were computed. Programs in this language therefore describe explanations of probabilistic reasoning problems. The language relies on a story-telling metaphor of explanation, where the reader is guided through a series of well-understood steps from some initial state to the final result. Programs can also be manipulated according to a set of laws to automatically generate equivalent explanations from one explanation instance. This increases the explanatory value of the language by allowing readers to cheaply derive alternative explanations if they do not understand the first. The language is composed of two parts: a formal textual notation for specifying explanation-producing programs and the more elaborate visual notation for presenting those explanations. We formally define the abstract syntax of explanations and define the semantics of the textual notation in terms of the explanations that are produced.     

Structural and probabilistic knowledge for abductive reasoning

Different ways of representing probabilistic relationships among the attributes of a domain ar examined, and it is shown that the nature of domain relationships used in a representation affects the types of reasoning objectives that can be achieved. Two well-known formalisms for representing the probabilistic among attributes of a domain. These are the dependence tree formalism presented by C.K. Chow and C.N. Liu (1968) and the Bayesian networks methodology presented by J. Pearl (1986). An example is used to illustrate the nature of the relationships and the difference in the types of reasoning performed by these two representations. An abductive type of reasoning objective that requires use of the known qualitative relationships of the domain is demonstrated. A suitable way to represent such qualitative relationships along with the probabilistic knowledge is given, and how an explanation for a set of observed events may be constituted is discussed. An algorithm for learning the qualitative relationships from empirical data using an algorithm based on the minimization of conditional entropy is presented     

Using analogical reasoning to design buildings

This paper presents a methodology for identifying the relevant design elements for the synthesis of new structural designs using previous design situations and their corresponding solutions. The study is a reflection of the observation that engineers use related experience when solving new problems. The methodology is an application of transformational analogy, a form of analogical reasoning.A prototype system STRUPLE has been designed to implement the methodology, making use of knowledge based expert systems techniques. The emphasis of STRUPLE differs from that of traditional expert systems in that the latter only use formalized o or compiled knowledge, whereas STRUPLE uses an experience data base as a knowledge supplement.     

Deciding probabilistic bisimilarity over infinite-state probabilistic systems

We prove that probabilistic bisimilarity is decidable over probabilistic extensions of BPA and BPP processes. For normed subclasses of probabilistic BPA and BPP processes we obtain polynomial-time algorithms. Further, we show that probabilistic bisimilarity between probabilistic pushdown automata and finite-state systems is decidable in exponential time. If the number of control states in PDA is bounded by a fixed constant, then the algorithm needs only polynomial time. The work has been supported by the research centre Institute for Theoretical Computer Science (ITI), project No. 1M0545.     

Uncertain inheritance and recognition as probabilistic default reasoning

This paper proposes probabilistic default reasoning as a suitable approach to uncertain inheritance and recognition for fuzzy and uncertain object-oriented models. The uncertainty is due to the uncertain membership of an object to a class and/or the uncertain applicability of a property, i.e., an attribute or a method, to a class. First, we introduce a logic-based uncertain object-oriented model where uncertain membership and applicability are measured by support pairs, which are lower and upper bounds on probability. The probability for a property being applicable to a class is interpreted as the conditional probability of the property being applicable to an object given that the object is a member of the class. Each uncertainty applicable property is then a default probabilistic logic rule, which is defeasible. In order to reduce the computational complexity of general probabilistic default reasoning, we propose to use Jeffrey's rule for a weaker notion of consistency and for local inference, then apply them to uncertain inheritance of attributes and methods. Using the same approach but with inverse Jeffrey's rule, uncertain recognition as probabilistic default reasoning is also presented. © 2001 John Wiley & Sons, Inc.     

Web robot detection: A probabilistic reasoning approach

In this paper, we introduce a probabilistic modeling approach for addressing the problem of Web robot detection from Web-server access logs. More specifically, we construct a Bayesian network that classifies automatically access log sessions as being crawler- or human-induced, by combining various pieces of evidence proven to characterize crawler and human behavior. Our approach uses an adaptive-threshold technique to extract Web sessions from access logs. Then, we apply machine learning techniques to determine the parameters of the probabilistic model. The resulting classification is based on the maximum posterior probability of all classes given the available evidence. We apply our method to real Web-server logs and obtain results that demonstrate the robustness and effectiveness of probabilistic reasoning for crawler detection.     

Algebraic reasoning for probabilistic action systems and while-loops

Back and von Wright have developed algebraic laws for reasoning about loops in a total correctness framework using the refinement calculus. We extend their work to reasoning about probabilistic loops in the probabilistic refinement calculus. We apply our algebraic reasoning to derive transformation rules for probabilistic action systems and probabilistic while-loops. In particular we focus on developing data refinement rules for these two constructs. Our extension is interesting since some well known transformation rules that are applicable to standard programs are not applicable to probabilistic ones: we identify some of these important differences and we develop alternative rules where possible.     

Incidence calculus: A mechanism for probabilistic reasoning

Mechanisms for the automation of uncertainty are required for expert systems. Sometimes these mechanisms need to obey the properties of probabilistic reasoning. We argue that a purely numeric mechanism, like those proposed so far, cannot provide a probabilistic logic with truth functional connectives. We propose an alternative mechanism, Incidence Calculus, which is based on a representation of uncertainty using sets of points, which might represent situations models or possible worlds. Incidence Calculus does provide a probabilistic logic with truth functional connectives.     

基于概率软逻辑的多层次识别和推理

随着全球老龄化人口增长,老年人的日常行为监管和护理也成为极具挑战性的社会问题。为了应对这种不断增长的社会需求,提出了一种由数据和知识共同驱动、使用概率软逻辑（Probabilistic Soft Logic）和多层次分析对老年人的日常活动进行建模的方法,来解决老年人护理中的活动识别问题。实验表明,该方法在活动识别和异常活动检测上,比隐马尔可夫模型能产生更高的精度,并且,该方法比非层次识别方法具有更快的响应速度。     

Using schedulers to test probabilistic distributed systems

Formal methods are one of the most important approaches to increasing the confidence in the correctness of software systems. A formal specification can be used as an oracle in testing since one can determine whether an observed behaviour is allowed by the specification. This is an important feature of formal testing: behaviours of the system observed in testing are compared with the specification and ideally this comparison is automated. In this paper we study a formal testing framework to deal with systems that interact with their environment at physically distributed interfaces, called ports, and where choices between different possibilities are probabilistically quantified. Building on previous work, we introduce two families of schedulers to resolve nondeterministic choices among different actions of the system. The first type of schedulers, which we call global schedulers, resolves nondeterministic choices by representing the environment as a single global scheduler. The second type, which we call localised schedulers, models the environment as a set of schedulers with there being one scheduler for each port. We formally define the application of schedulers to systems and provide and study different implementation relations in this setting.     

Using approximate reasoning to represent default knowledge

We discuss the issue of default inference rules. We introduce the reasoning mechanism of the theory of approximate reasoning. We show how we can represent default knowledge in the framework of this theory.     

Concept over time: the combination of probabilistic topic model with wikipedia knowledge

Probabilistic topic models could be used to extract low-dimension aspects from document collections, and capture how the aspects change over time. However, such models without any human knowledge often produce aspects that are not interpretable. In recent years, a number of knowledge-based topic models and dynamic topic models have been proposed, but they could not process concept knowledge and temporal information in Wikipedia. In this paper, we fill this gap by proposing a new probabilistic modeling framework which combines both data-driven topic model and Wikipedia knowledge. With the supervision of Wikipedia knowledge, we could grasp more coherent aspects, namely, concepts, and detect the trends of concepts more accurately, the detected concept trends can reflect bursty content in text and people’s concern. Our method could detect events and discover events specific entities in text. Experiments on New York Times and TechCrunch datasets show that our framework outperforms two baselines.     

Data analysis using scale-space filtering and Bayesian probabilistic reasoning

This paper describes a program for the analysis of output curves from a differential thermal analyzer (DTA). The program first extracts probabilistic qualitative features from a DTA curve of a soil sample, and then uses Bayesian probabilistic reasoning to infer what minerals are present in the soil. It consists of a qualifier module and a classifier module. The qualifier employs a simple and efficient extension of scale-space filtering DTA data. Ordinarily when filtering operations are not highly accurate, points can vanish from contours in the scale-space image. To handle the problem of vanishing points, our algorithm uses perceptual organization heuristics to group the points into lines. It then groups these lines into contours by using additional heuristics. Probabilities are associated with these contours using domain-specific correlations. A Bayes tree classifier processes probabilistic features to infer the presence of different minerals in the soil. We show experimentally that using domain-specific correlations to infer qualitative features, this algorithm outperforms a domain-independent algorithm that does not.     

Multi-Modal Reasoning medical diagnosis system integrated with probabilistic reasoning

In this paper, a Multi Modal Reasoning (MMR) method integrated with probabilistic reasoning is proposed for the diagnosis support module of the open eHealth platform. MMR is based on both Rule Based Reasoning (RBR) and Case Based Reasoning (CBR). It is not only applied to the identification of diseases and syndromes based on medical guidelines, but also deals with exceptional cases and individual therapies in order to improve diagnostic accuracy. Moreover, a new rule expression frame is introduced to deal with uncertainty, which can represent and process vague, imprecise, and incomplete information. Furthermore, this system is capable of updating the attributes of rules and inducing rules with a small data sample.