Bounding probabilistic relationships in Bayesian networks using qualitative influences: methods and applications

AbstractWe present conditions under which one can bound the probabilistic relationships between random variables in a Bayesian network by exploiting known or induced qualitative relationships. Generic strengthening and weakening operations produce bounds on cumulative distributions, and the directions of these bounds are maintained through qualitative influences. We show how to incorporate these operations in a state-space abstraction method, so that bounds provably tighten as an approximate network is refined. We apply these techniques to qualitative tradeoff resolution demonstrating an ability to identify qualitative relationships among random variables without exhaustively using the probabilistic information encoded in the given network. In an application to path planning, we present an anytime algorithm with run-time computable error bounds

Using visualization, variable selection and feature extraction to learn from industrial data

Although the engineers of industry have access to process data, they seldom use advanced statistical tools to solve process control problems. Why this reluctance? I believe that the reason is in the history of the development of statistical tools, which were developed in the era of rigorous mathematical modelling, manual computation and small data sets. This created sophisticated tools. The engineers do not understand the requirements of these algorithms related, for example, to pre-processing of data. If algorithms are fed with unsuitable data, or parameterized poorly, they produce unreliable results, which may lead an engineer to turn down statistical analysis in general. This thesis looks for algorithms that probably do not impress the champions of statistics, but serve process engineers. This thesis advocates three properties in an algorithm: supervised operation, robustness and understandability. Supervised operation allows and requires the user to explicate the goal of the analysis, which allows the algorithm to discover results that are relevant to the user. Robust algorithms allow engineers to analyse raw process data collected from the automation system of the plant. The third aspect is understandability: the user must understand how to parameterize the model, what is the principle of the algorithm, and know how to interpret the results. The above criteria are justified with the theories of human learning. The basis is the theory of constructivism, which defines learning as construction of mental models. Then I discuss the theories of organisational learning, which show how mental models influence the behaviour of groups of persons. The next level discusses statistical methodologies of data analysis, and binds them to the theories of organisational learning. The last level discusses individual statistical algorithms, and introduces the methodology and the algorithms proposed by this thesis. This methodology uses three types of algorithms: visualization, variable selection and feature extraction. The goal of the proposed methodology is to reliably and understandably provide the user with information that is related to a problem he has defined interesting. The above methodology is illustrated by an analysis of an industrial case: the concentrator of the Hitura mine. This case illustrates how to define the problem with off-line laboratory data, and how to search the on-line data for solutions. A major advantage of algorithmic study of data is efficiency: the manual approach reported in the early took approximately six man months; the automated approach of this thesis created comparable results in few weeks.reviewe

Supply chain risk analysis

A new decision support system is proposed and developed that will help sustaining business in a high-risk business environment. The system is developed as a web application to better integrate the supply chain entities and to provide a common platform for performing risk analysis in a supply chain. The system performs a risk analysis and calculates risk factor with each activity in the supply considering its interrelationship with other activities. Bayesian networks along with fault tree structures are embedded in the system and logical rules are used to perform a qualitative fault tree analysis, as the data required to calculate the frequency of occurrence is rarely available. The developed system guides the risk assessment process: from asset identification to consequence analysis before estimating the risk factor associated with each activity in the supply chain. The system is tested with a sample case study on a highly explosive product. Results show that the system is capable of identifying high-risk threats. The system further needs to be developed to add a safeguard analysis module and to enable automatic data extraction from the enterprise resource planning and legacy databases. It is expected that the system on complete development and induction will help supply chain managers to manage business risks and operations more efficiently and effectively by providing a complete picture of the risk environment and safeguards required to reduce the risk level

Naval Aviation Squadron Risk Analysis Predictive Bayesian Network Modeling Using Maintenance Climate Assessment Survey Results

Associated risks in flying have resulted in injury or death to aircrew and passengers, and damage or destruction of the aircraft and its surroundings. Although the Naval Aviation\u27s flight mishap rate declined over the past 60 years, the proportion of human error causal factors has stayed relatively constant at about 80%. Efforts to reduce human errors have focused attention on understanding the aircrew and maintenance actions occurring in complex systems. One such tool has been the Naval Aviation squadrons\u27 regular participation in survey questionnaires deigned to measure respondent ratings related to personal judgments or perceptions of organizational climate for meeting the extent to which a particular squadron achieved the High Reliability Organization (HRO) criteria of achieving safe and reliable operations and maintenance practices while working in hazardous environments. Specifically, the Maintenance Climate Assessment Survey (MCAS) is completed by squadron maintainers to enable leadership to assess their unit\u27s aggregated responses against those from other squadrons. Bayesian Network Modeling and Simulation provides a potential methodology to represent the relationships of MCAS results and mishap occurrences that can be used to derive and calculate probabilities of incurring a future mishap. Model development and simulation analysis was conducted to research a causal relationship through quantitative analysis of conditional probabilities based upon observed evidence of previously occurred mishaps. This application would enable Navy and Marine Corps aviation squadron leadership to identify organizational safety risks, apply focused proactive measures to mitigate related hazards characterized by the MCAS results, and reduce organizational susceptibility to future aircraft mishaps

A Bayesian belief network computational model of social capital in virtual communities

The notion of social capital (SC) is increasingly used as a framework for describing social issues in terrestrial communities. For more than a decade, researchers use the term to mean the set of trust, institutions, social norms, social networks, and organizations that shape the interactions of actors within a society and that are considered to be useful and assets for communities to prosper both economically and socially. Despite growing popularity of social capital especially, among researchers in the social sciences and the humanities, the concept remains ill-defined and its operation and benefits limited to terrestrial communities. In addition, proponents of social capital often use different approaches to analyze it and each approach has its own limitations. This thesis examines social capital within the context of technology-mediated communities (also known as virtual communities) communities. It presents a computational model of social capital, which serves as a first step in the direction of understanding, formalizing, computing and discussing social capital in virtual communities. The thesis employs an eclectic set of approaches and procedures to explore, analyze, understand and model social capital in two types of virtual communities: virtual learning communities (VLCs) and distributed communities of practice (DCoP). There is an intentional flow to the analysis and the combination of methods described in the thesis. The analysis includes understanding what constitutes social capital in the literature, identifying and isolating variables that are relevant to the context of virtual communities, conducting a series of studies to further empirically examine various components of social capital identified in three kinds of virtual communities and building a computational model. A sensitivity analysis aimed at examining the statistical variability of the individual variables in the model and their effects on the overall level of social capital are conducted and a series of evidence-based scenarios are developed to test and update the model. The result of the model predictions are then used as input to construct a final empirical study aimed at verifying the model.Key findings from the various studies in the thesis indicated that SC is a multi-layered, multivariate, multidimensional, imprecise and ill-defined construct that has emerged from a rather murky swamp of terminology but it is still useful for exploring and understanding social networking issues that can possibly influence our understanding of collaboration and learning in virtual communities. Further, the model predictions and sensitivity analysis suggested that variables such as trust, different forms of awareness, social protocols and the type of the virtual community are all important in discussion of SC in virtual communities but each variable has different level of sensitivity to social capital. The major contributions of the thesis are the detailed exploration of social capital in virtual communities and the use of an integrated set of approaches in studying and modelling it. Further, the Bayesian Belief Network approach applied in the thesis can be extended to model other similar complex online social systems

An Overview of Some Recent Developments in Bayesian Problem Solving Techniques

The last five years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to engineering their knowledge bases. This special issue reviews recent research in Bayesian problem-solving techniques. The articles cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory. Here, I provide a brief introduction to Bayesian networks and then cover applications of Bayesian problem-solving techniques, knowledge-based model construction and structured representations, and the learning of graphical probability models. The past five years or so have seen increased interest and tremendous..

Learning Bayesian networks using evolutionary computation and its application in classification.

by Lee Shing-yan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 126-133).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Statement --- p.4Chapter 1.2 --- Contributions --- p.4Chapter 1.3 --- Thesis Organization --- p.5Chapter 2 --- Background --- p.7Chapter 2.1 --- Bayesian Networks --- p.7Chapter 2.1.1 --- A Simple Example [42] --- p.8Chapter 2.1.2 --- Formal Description and Notations --- p.9Chapter 2.1.3 --- Learning Bayesian Network from Data --- p.14Chapter 2.1.4 --- Inference on Bayesian Networks --- p.18Chapter 2.1.5 --- Applications of Bayesian Networks --- p.19Chapter 2.2 --- Bayesian Network Classifiers --- p.20Chapter 2.2.1 --- The Classification Problem in General --- p.20Chapter 2.2.2 --- Bayesian Classifiers --- p.21Chapter 2.2.3 --- Bayesian Network Classifiers --- p.22Chapter 2.3 --- Evolutionary Computation --- p.28Chapter 2.3.1 --- Four Kinds of Evolutionary Computation --- p.29Chapter 2.3.2 --- Cooperative Coevolution --- p.31Chapter 3 --- Bayesian Network Learning Algorithms --- p.33Chapter 3.1 --- Related Work --- p.34Chapter 3.1.1 --- Using GA --- p.34Chapter 3.1.2 --- Using EP --- p.36Chapter 3.1.3 --- Criticism of the Previous Approaches --- p.37Chapter 3.2 --- Two New Strategies --- p.38Chapter 3.2.1 --- A Hybrid Framework --- p.38Chapter 3.2.2 --- A New Operator --- p.39Chapter 3.3 --- CCGA --- p.44Chapter 3.3.1 --- The Algorithm --- p.45Chapter 3.3.2 --- CI Test Phase --- p.46Chapter 3.3.3 --- Cooperative Coevolution Search Phase --- p.47Chapter 3.4 --- HEP --- p.52Chapter 3.4.1 --- A Novel Realization of the Hybrid Framework --- p.54Chapter 3.4.2 --- Merging in HEP --- p.55Chapter 3.4.3 --- Prevention of Cycle Formation --- p.55Chapter 3.5 --- Summary --- p.56Chapter 4 --- Evaluation of Proposed Learning Algorithms --- p.57Chapter 4.1 --- Experimental Methodology --- p.57Chapter 4.2 --- Comparing the Learning Algorithms --- p.61Chapter 4.2.1 --- Comparing CCGA with MDLEP --- p.63Chapter 4.2.2 --- Comparing HEP with MDLEP --- p.65Chapter 4.2.3 --- Comparing CCGA with HEP --- p.68Chapter 4.3 --- Performance Analysis of CCGA --- p.70Chapter 4.3.1 --- Effect of Different α --- p.70Chapter 4.3.2 --- Effect of Different Population Sizes --- p.72Chapter 4.3.3 --- Effect of Varying Crossover and Mutation Probabilities --- p.73Chapter 4.3.4 --- Effect of Varying Belief Factor --- p.76Chapter 4.4 --- Performance Analysis of HEP --- p.77Chapter 4.4.1 --- The Hybrid Framework and the Merge Operator --- p.77Chapter 4.4.2 --- Effect of Different Population Sizes --- p.80Chapter 4.4.3 --- Effect of Different --- p.81Chapter 4.4.4 --- Efficiency of the Merge Operator --- p.84Chapter 4.5 --- Summary --- p.85Chapter 5 --- Learning Bayesian Network Classifiers --- p.87Chapter 5.1 --- Issues in Learning Bayesian Network Classifiers --- p.88Chapter 5.2 --- The Multinet Classifier --- p.89Chapter 5.3 --- The Augmented Bayesian Network Classifier --- p.91Chapter 5.4 --- Experimental Methodology --- p.94Chapter 5.5 --- Experimental Results --- p.97Chapter 5.6 --- Discussion --- p.103Chapter 5.7 --- Application in Direct Marketing --- p.106Chapter 5.7.1 --- The Direct Marketing Problem --- p.106Chapter 5.7.2 --- Response Models --- p.108Chapter 5.7.3 --- Experiment --- p.109Chapter 5.8 --- Summary --- p.115Chapter 6 --- Conclusion --- p.116Chapter 6.1 --- Summary --- p.116Chapter 6.2 --- Future Work --- p.118Chapter A --- A Supplementary Parameter Study --- p.120Chapter A.1 --- Study on CCGA --- p.120Chapter A.1.1 --- Effect of Different α --- p.120Chapter A.1.2 --- Effect of Different Population Sizes --- p.121Chapter A.1.3 --- Effect of Varying Crossover and Mutation Probabilities --- p.121Chapter A.1.4 --- Effect of Varying Belief Factor --- p.122Chapter A.2 --- Study on HEP --- p.123Chapter A.2.1 --- The Hybrid Framework and the Merge Operator --- p.123Chapter A.2.2 --- Effect of Different Population Sizes --- p.124Chapter A.2.3 --- Effect of Different Δα --- p.124Chapter A.2.4 --- Efficiency of the Merge Operator --- p.12

Designing Embodied Interactive Software Agents for E-Learning: Principles, Components, and Roles

Embodied interactive software agents are complex autonomous, adaptive, and social software systems with a digital embodiment that enables them to act on and react to other entities (users, objects, and other agents) in their environment through bodily actions, which include the use of verbal and non-verbal communicative behaviors in face-to-face interactions with the user. These agents have been developed for various roles in different application domains, in which they perform tasks that have been assigned to them by their developers or delegated to them by their users or by other agents. In computer-assisted learning, embodied interactive pedagogical software agents have the general task to promote human learning by working with students (and other agents) in computer-based learning environments, among them e-learning platforms based on Internet technologies, such as the Virtual Linguistics Campus (www.linguistics-online.com). In these environments, pedagogical agents provide contextualized, qualified, personalized, and timely assistance, cooperation, instruction, motivation, and services for both individual learners and groups of learners. This thesis develops a comprehensive, multidisciplinary, and user-oriented view of the design of embodied interactive pedagogical software agents, which integrates theoretical and practical insights from various academic and other fields. The research intends to contribute to the scientific understanding of issues, methods, theories, and technologies that are involved in the design, implementation, and evaluation of embodied interactive software agents for different roles in e-learning and other areas. For developers, the thesis provides sixteen basic principles (Added Value, Perceptible Qualities, Balanced Design, Coherence, Consistency, Completeness, Comprehensibility, Individuality, Variability, Communicative Ability, Modularity, Teamwork, Participatory Design, Role Awareness, Cultural Awareness, and Relationship Building) plus a large number of specific guidelines for the design of embodied interactive software agents and their components. Furthermore, it offers critical reviews of theories, concepts, approaches, and technologies from different areas and disciplines that are relevant to agent design. Finally, it discusses three pedagogical agent roles (virtual native speaker, coach, and peer) in the scenario of the linguistic fieldwork classes on the Virtual Linguistics Campus and presents detailed considerations for the design of an agent for one of these roles (the virtual native speaker)

To appear: AI Magazine Special Issue on Uncertainty in AI, Summer 1999. Introduction to This Special Issue: An Overview of Some Recent Developments in Bayesian Problem Solving Techniques

The last five years have seen a surge in interest in the use of techniques from Bayesian decision theory to address problems in AI. Decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. Within the context of this framework, researchers in uncertainty in the AI community have been developing computational techniques for building rational agents and representations suited to engineering their knowledge bases. This special issue reviews recent research in Bayesian problem-solving techniques. The articles cover the topics of inference in Bayesian networks, decision-theoretic planning, and qualitative decision theory. Here, I provide a brief introduction to Bayesian networks and then cover applications of Bayesian problem-solving techniques, knowledge-based model construction and structured representations, and the learning of graphical probability models. The past five years or so have seen increased interest and tremendous progress in the development of Bayesian techniques for building problem solving systems. We have come a long way since the Uncertainty in AI Workshop was founded in 1985, an event precipitated in large part by the fact that the mainstream AI community at that time considered probabilistic approaches impractical for building intelligent systems. Since then the workshop has become the Conference on Uncertainty in AI, attracting high-quality contributions from researchers in a broad array of disciplines, including AI, statistics, operations research, and decision science. In the last several years, concepts from Bayesian Decision Theory, along with representational and computational techniques developed within the Uncertainty in AI community have found their way into mainstream AI and are ap..