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.     

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.     

Information tradeoffs in model building: A network routing application

An integral part of the model-building process is the modeler's choice of how much information to gather and encode in the decision model. Obtaining more detailed and accurate information enables a more precise problem representation which, in turn, leads to more effective decision making. However, acquiring extensive and accurate information entails higher costs and delays. This paper uses a network routing decision context to illustrate the tradeoff between model precision and decision effectiveness, and explores a formal decision-theoretic approach to determine an appropriate model specification that balances information gathering costs and decision quality. We propose optimal and heuristic methods for generating good information search strategies, and report computational results based on random test problems. Our results highlight the importance of simultaneously considering information costs and decision payoffs for constructing a decision model to support routing decisions. The issues raised in this paper are especially significant for modeling dynamic, real-time decision contexts where delays induced by information gathering activities could have significant economic impact.     

Expert systems as extensions of the human mind: A user oriented,holistic approach to the design of multiple reasoning system environments and interfaces

Expert systems have had little impact as computing artifacts. In this paper we argue that the reason for this stems from the underlying assumption of most builders of expert systems that an expert system needs to acquire information and to control the interaction between the human user and itself. We show that this assumption has serious linguistic and usability flaws which diminish the likelihood of producing socially acceptable expert systems. We propose a reversal of this paradigm, for the design of expert systems, by assuming that it is the human user who needs to acquire information and to control the interaction between themselves and the system.     

Information systems management and strategy formulation: the 'stages of growth' model revisited

Abstract. The original 'stages of growth' model, as applied to the field of Information Systems (Gibson & Nolan, 1974), may be viewed as seminal, given the influence it has had on both theory and practice (Nolan, 1984; Sullivan, 1985; Ward, et al., 1990). This is so, despite the model's lack of substantiation based on empirical evidence, the overly simplistic assumptions on which it is based, and the limited focus of the original concept (Benbasat, et al., 1984; King & Kraemer, 1984).
Since its first appearance, the model has been refined following many applications (Nolan, 1979, 1984). In addition, others have developed 'stages of growth' models of their own. For example, Somogyi & Galliers, (1987a, b)provide a model depicting the move from basic data processing systems to strategic information systems, tracing the early days of commercial applications of computing through to the present day (Ward et al. 1990). Earl (1983, 1986, 1988, 1989) proposes a stages-model for information systems planning, as does Bhabuta (1988), while Hirschheim, et al. (1988) have developed a more broadly focused model associated with the development of the information-systems management function.
As a result of a review of the above and of a case study research undertaken in Perth, Western Australia, a revised model was developed, which takes account of current thinking and past experience in the application of the various 'stages of growth' models (Sutherland & Galliers, 1989).
This paper sets out to review some of these models and proceeds to describe the process of developing, testing and applying the revised 'stages of growth' model. The model is more broadly focused than the original concept, incorporating strategic, organizational, human resource and management considerations. As a result, it is argued that this model has greater utility than its predecessors.     

A Dempster-Shafer theory and uninorm-based framework of reasoning and multiattribute decision-making for surveillance system

Closed-circuit television and sensor-based intelligent surveillance systems have attracted considerable attentions in the field of public security affairs. To provide real-time reaction in the case of a huge volume of the surveillance data, researchers have proposed event-reasoning frameworks for modeling and inferring events of interest. However, they do not support decision-making, which is very important for surveillance operators. To this end, this paper incorporate a function of decision-making in an event-reasoning framework, so that our model not only can perform event-reasoning but also can predict, rank, and alarm threats according to uncertain information from multiple heterogeneous sources. In particular, we propose a multiattribute decision-making model, in which an object being watched is modeled as a multiattribute event, where each attribute corresponds to a specific source, and the information from each source can be used to elicit a local threat degree of different malicious situations with respect to the corresponding attribute. Moreover, to assess an overall threat degree of an object being observed, we also propose a method to fuse the conflict threat degrees regarding all the relevant attributes. Finally, we demonstrate the effectiveness of our framework by an airport security surveillance scenario.     

Belief, information acquisition, and trust in multi-agent systems—A modal logic formulation

In this paper, we consider the influence of trust on the assimilation of acquired information into an agent's belief. By use of modal logic, we semantically and axiomatically characterize the relationship among belief, information acquisition and trust. The belief and information acquisition operators are respectively represented by KD45 and KD normal modalities, whereas trust is denoted by a modal operator with minimal semantics. One characteristic axiom of the basic system is if agent i believes that agent j has told him the truth of p and he trusts the judgement of j on p, then he will also believe p. In addition to the basic system, some variants and further axioms for trust and information acquisition are also presented to show the expressive richness of the logic. The applications of the logic to computer security and database reasoning are also suggested by its connection with some previous works.     

Modeling signal-based decisions in online search environments: A non-recursive forward-looking approach

Consider a rational decision maker (DM) who must acquire a finite amount of information sequentially online from a set of products. The DM receives signals on the distribution of the product characteristics. Each time an observation is acquired, DMs redefine the probability of improving upon the products observed. The resulting information acquisition process depends on the values of the characteristics observed, the number and potential realizations of the remaining observations, and the type of signal received. We construct two functions determining the information acquisition behavior of DMs and illustrate numerically the importance of the characteristic on which signals are issued.     

Understanding users’ motivations to engage in virtual worlds: A multipurpose model and empirical testing

Despite the growth and commercial potential of virtual worlds, relatively little is known about what drives users’ motivations to engage in virtual worlds. This paper proposes and empirically tests a conceptual model aimed at filling this research gap. Given the multipurpose nature of virtual words the model integrates extrinsic and intrinsic motivation as behavioral determinants. By making use of the literature on information system value and motivation theory four important system-specific virtual world characteristics (economic value, ease of use, escapism, visual attractiveness) are added as motivational drivers. Using structural equation modeling on a sample of 846 users of the virtual world Second Life the hypotheses were tested. The results support the model; they confirm the role of extrinsic and intrinsic motivation as behavioral determinants and show how and to what extent the four system-specific elements function as motivational basis. Implications for research and practice are discussed.     

Development of expert control systems: A pattern classification and recognition approach

In this paper, a pattern classification and recognition approach to expert control systems is developed for use in the on-line analysis and design of dynamic systems. The approach used is based on the tuning of a three-term PID controller and, hence, it is not dependent on a specific form of the process model. A real-time experiment of implementing the developed controller using a microcomputer and associated hardware is presented. A sample set of production rules is discussed. The expert system reaches appropriate tuning parameters, using extracted features, such as oscillatory, underdamped, and exponentially monotonic properties.     

A robust model predictive control algorithm for incrementally conic uncertain/nonlinear systems

This paper presents a robustly stabilizing model predictive control algorithm for systems with incrementally conic uncertain/nonlinear terms and bounded disturbances. The resulting control input consists of feedforward and feedback components. The feedforward control generates a nominal trajectory from online solution of a finite‐horizon constrained optimal control problem for a nominal system model. The feedback control policy is designed off‐line by utilizing a model of the uncertainty/nonlinearity and establishes invariant ‘state tubes’ around the nominal system trajectories. The entire controller is shown to be robustly stabilizing with a region of attraction composed of the initial states for which the finite‐horizon constrained optimal control problem is feasible for the nominal system. Synthesis of the feedback control policy involves solution of linear matrix inequalities. An illustrative numerical example is provided to demonstrate the control design and the resulting closed‐loop system performance. Copyright © 2010 John Wiley & Sons, Ltd.     

Two approaches to developing expert systems: A consideration of formal and semi-formal domains

The conventional approach to developing expert systems views the domain of application as being formally defined. This view often leads to practical problems when expert systems are built using this approach. This paper examines the implications and problems of the formal approach to expert system design and proposes an alternative approach based on the concept of semi-formal domains. This approach, which draws on the work of socio-technical information systems, provides guidelines which can be used for the design of successful expert systems.     

Security lapses and the omission of information security measures: A threat control model and empirical test

Organizations and individuals are increasingly impacted by misuses of information that result from security lapses. Most of the cumulative research on information security has investigated the technical side of this critical issue, but securing organizational systems has its grounding in personal behavior. The fact remains that even with implementing mandatory controls, the application of computing defenses has not kept pace with abusers’ attempts to undermine them. Studies of information security contravention behaviors have focused on some aspects of security lapses and have provided some behavioral recommendations such as punishment of offenders or ethics training. While this research has provided some insight on information security contravention, they leave incomplete our understanding of the omission of information security measures among people who know how to protect their systems but fail to do so. Yet carelessness with information and failure to take available precautions contributes to significant civil losses and even to crimes. Explanatory theory to guide research that might help to answer important questions about how to treat this omission problem lacks empirical testing. This empirical study uses protection motivation theory to articulate and test a threat control model to validate assumptions and better understand the “knowing-doing” gap, so that more effective interventions can be developed.     

OpenClinical.net: A platform for creating and sharing knowledge and promoting best practice in healthcare

It is well known that medical knowledge is growing so rapidly that it is difficult or impossible for healthcare professionals to keep up. More and more techniques for diagnosing and treating diseases are becoming available, yet new research findings and clinical practices are slow to spread. Information technology and the internet are providing important new ways of disseminating knowledge in healthcare as in many other domains. Knowledge engineering techniques for supporting decision-making and process management are also becoming available, and can be used to support busy clinicians, helping to ensure that their decisions are consistent with current knowledge and clinical procedures are carried out in a timely, efficient and safe way. The OpenClinical.net project is building on these techniques to demonstrate a new paradigm for disseminating knowledge and promoting best practice. The key idea is that much professional expertise can be modelled as computer-interpretable knowledge and used to assist decision-making, workflow management, communication and coordination of care and many other professional tasks. The central goal of OpenClinical.net is to demonstrate how this might be done at scale, through a form of “crowd sourcing”, in order to create and maintain a sharable knowledge base that is available in an open access and open source repository. This paper provides an overview of the project and a summary of progress to date.     

An entropy approach to disclosure risk assessment: Lessons from real applications and simulated domains

We live in an increasingly mobile world, which leads to the duplication of information across domains. Though organizations attempt to obscure the identities of their constituents when sharing information for worthwhile purposes, such as basic research, the uncoordinated nature of such environment can lead to privacy vulnerabilities. For instance, disparate healthcare providers can collect information on the same patient. Federal policy requires that such providers share “de-identified” sensitive data, such as biomedical (e.g., clinical and genomic) records. But at the same time, such providers can share identified information, devoid of sensitive biomedical data, for administrative functions. On a provider-by-provider basis, the biomedical and identified records appear unrelated, however, links can be established when multiple providers' databases are studied jointly. The problem, known as trail disclosure, is a generalized phenomenon and occurs because an individual's location access pattern can be matched across the shared databases. Due to technical and legal constraints, it is often difficult to coordinate between providers and thus it is critical to assess the disclosure risk in distributed environments, so that we can develop techniques to mitigate such risks. Research on privacy protection has so far focused on developing technologies to suppress or encrypt identifiers associated with sensitive information. There is a growing body of work on the formal assessment of the disclosure risk of database entries in publicly shared databases, but less attention has been paid to the distributed setting. In this research, we review the trail disclosure problem in several domains with known vulnerabilities and show that disclosure risk is influenced by the distribution of how people visit service providers. Based on empirical evidence, we propose an entropy metric for assessing such risk in shared databases prior to their release. This metric assesses risk by leveraging the statistical characteristics of a visit distribution, as opposed to person-level data. It is computationally efficient and superior to existing risk assessment methods, which rely on ad hoc assessment that are often computationally expensive and unreliable. We evaluate our approach on a range of location access patterns in simulated environments. Our results demonstrate that the approach is effective at estimating trail disclosure risks and the amount of self-information contained in a distributed system is one of the main driving factors.     

Supporting decision support: Where information on DSS is located

DSS professionals may differ in their opinion and practice as to where they locate the most useful information relevant to their work. Online and other electronic form databases are increasingly becoming the key resource for literature searches. This study empirically compared 31 online databases identified as promising for DSS relevant information according to their coverage of DSS. Rankings for recent years and temporally unconstrained conditions were obtained and discussed. INSPEC was the highest ranked database overall and for recent information. INSPEC was also the highest rated database for coverage of major DSS journals. However, there are many other databases that also provide coverage of DSS materials. It is hoped that DSS professionals will use these results to improve the effectiveness of their information search process.     

Movie forecast Guru: A Web-based DSS for Hollywood managers

Herein we describe a Web-based DSS to help Hollywood managers make better decisions on important movie characteristics, such as, genre, super stars, technical effects, release time, etc. These parameters are used to build prediction models to classify a movie in one of nine success categories, from a “flop” to a “blockbuster”. The system employs a number of traditional and non-traditional prediction models as distributed independent experts, implemented as Web services. The paper describes the purpose and the architecture of the system, the development environment, the user assessment results, and the lessons learned as they relate to Web-based DSS development.     

GENETIC ALGORITHMS: A Search Technique Applied to Behavior Analysis

Abstract

Genetic algorithms are powerful generalized search techniques. This paper shows that genetic algorithms can solve a difficult class of problems in general systems theory quickly and efficiently. Genetic algorithms appear to be ideally suited to solving the combinatorially complex problem of behavior analysis. The search space of behavior analysis experiences exponential growth as a function of the number of variables. The genetic algorithm converges after considering a small percentage of these potential solutions. The number of solutions that need to be examined by the genetic algorithm seems to be a polynomial function of the number of variables and, in fact, the growth appears to be linear