B2DI a bayesian BDI agent model with causal belief updating based on MSBN

In this paper, we introduce B2DI model that extends BDI model to perform Bayesian inference under uncertainty. For scalability and flexibility purposes, Multiply Sectioned Bayesian Network (MSBN) technology has been selected and adapted to BDI agent reasoning. A belief update mechanism has been defined for agents, whose belief models are connected by public shared beliefs, and the certainty of these beliefs is updated based on MSBN. The classical BDI agent architecture has been extended in order to manage uncertainty using Bayesian reasoning. The resulting extended model, so-called B2DI, proposes a new control loop. The proposed B2DI model has been evaluated in a network fault diagnosis scenario. The evaluation has compared this model with two previously developed agent models. The evaluation has been carried out with a real testbed diagnosis scenario using JADEX. As a result, the proposed model exhibits significant improvements in the cost and time required to carry out a reliable diagnosis.

El daño y el nexo causal en la perdida de oportunidad

El presente escrito se ocupa de estudiar el nexo de causalidad como elemento estructural de la responsabilidad cuando éste es difuso. Para ello, se pretende abordar la pérdida de la oportunidad como una teoría especial de causalidad que tiene lugar cuando el nexo causal no resulta claro, lo cual contradice la tesis preponderante de la doctrina y la jurisprudencia tradicional según la cual, la pérdida de la oportunidad es un criterio autónomo del daño. En su contenido se realiza una explicación del por qué se entiende la pérdida de la oportunidad como una teoría especial de causalidad y no como un criterio autónomo de daño, haciendo énfasis en el elemento de certeza que caracteriza al daño. Posteriormente, se advierte del tratamiento que la jurisprudencia le ha dado a la pérdida de la oportunidad. A su turno, el presente documento, indica la naturaleza jurídica de la pérdida de la oportunidad, afirmando que es una inferencia lógica que realiza el juez y no un hecho que altere el estado de las cosas como si sucede con el daño. Finalmente, se aborda la prueba de la teoría de la pérdida de la oportunidad mediante un cálculo de probabilidades y se identifican los pasos para realizar una adecuada reparación integral.

Bayesian versus frequentist statistical inference for investi gating a one-off cancer cluster reported to a health department

Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.

High-level concept annotation using ontology and probabilistic inference

Image annotation is a significant step towards semantic based image retrieval. Ontology is a popular approach for semantic representation and has been intensively studied for multimedia analysis. However, relations among concepts are seldom used to extract higher-level semantics. Moreover, the ontology inference is often crisp. This paper aims to enable sophisticated semantic querying of images, and thus contributes to 1) an ontology framework to contain both visual and contextual knowledge, and 2) a probabilistic inference approach to reason the high-level concepts based on different sources of information. The experiment on a natural scene database from LabelMe database shows encouraging results.

Mid-level concept learning with visual contextual ontologies and probabilistic inference for image annotation

To date, automatic recognition of semantic information such as salient objects and mid-level concepts from images is a challenging task. Since real-world objects tend to exist in a context within their environment, the computer vision researchers have increasingly incorporated contextual information for improving object recognition. In this paper, we present a method to build a visual contextual ontology from salient objects descriptions for image annotation. The ontologies include not only partOf/kindOf relations, but also spatial and co-occurrence relations. A two-step image annotation algorithm is also proposed based on ontology relations and probabilistic inference. Different from most of the existing work, we specially exploit how to combine representation of ontology, contextual knowledge and probabilistic inference. The experiments show that image annotation results are improved in the LabelMe dataset.

A fully Bayesian approach to inference for Coxian phase-type distributions with covariate dependent mean

Phase-type distributions represent the time to absorption for a finite state Markov chain in continuous time, generalising the exponential distribution and providing a flexible and useful modelling tool. We present a new reversible jump Markov chain Monte Carlo scheme for performing a fully Bayesian analysis of the popular Coxian subclass of phase-type models; the convenient Coxian representation involves fewer parameters than a more general phase-type model. The key novelty of our approach is that we model covariate dependence in the mean whilst using the Coxian phase-type model as a very general residual distribution. Such incorporation of covariates into the model has not previously been attempted in the Bayesian literature. A further novelty is that we also propose a reversible jump scheme for investigating structural changes to the model brought about by the introduction of Erlang phases. Our approach addresses more questions of inference than previous Bayesian treatments of this model and is automatic in nature. We analyse an example dataset comprising lengths of hospital stays of a sample of patients collected from two Australian hospitals to produce a model for a patient's expected length of stay which incorporates the effects of several covariates. This leads to interesting conclusions about what contributes to length of hospital stay with implications for hospital planning. We compare our results with an alternative classical analysis of these data.

Contradictions : Genre, causal layered analysis and analysis of institutional change

In what follows, I put forward an argument for an analytical method for social science that operates at the level of genre. I argue that generic convergence, generic hybridity, and generic instability provide us with a powerful perspectives on changes in political, cultural, and economic relationships, most specifically at the level of institutions. Such a perspective can help us identify the transitional elements, relationships, and trajectories that define the place of our current system in history, thereby grounding our understanding of possible futures.1 In historically contextualising our present with this method, my concern is to indicate possibilities for the future. Systemic contradictions indicate possibility spaces within which systemic change must and will emerge. We live in a system currently dominated by many fully-expressed contradictions, and so in the presence of many possible futures. The contradictions of the current age are expressed most overtly in the public genres of power politics. Contemporary public policy—indeed politics in general-is an excellent focus for any investigation of possible futures, precisely because of its future-oriented function. It is overtly hortatory; it is designed ‘to get people to do things’ (Muntigl in press: 147). There is no point in trying to get people to do things in the past. Consequently, policy discourse is inherently oriented towards creating some future state of affairs (Graham in press), along with concomitant ways of being, knowing, representing, and acting (Fairclough 2000).

The causal effect of income on health: Evidence from German reunification

We investigate whether therewas a causal effect of income changes on the health satisfaction of East and West Germans in the years following reunification. Our data source is the German Socio-Economic Panel (GSOEP) between 1984 and 2002, and we fit a recently proposed fixed-effects ordinal estimator to our health measures and use a causal decomposition technique to account for panel attrition.We find evidence of a significant positive effect of income changes on health satisfaction, but the quantitative size of this effect is small. This is the case with respect to current income and a measure of ‘permanent’ income.

Privacy and trust management for electronic health records

Establishing a nationwide Electronic Health Record system has become a primary objective for many countries around the world, including Australia, in order to improve the quality of healthcare while at the same time decreasing its cost. Doing so will require federating the large number of patient data repositories currently in use throughout the country. However, implementation of EHR systems is being hindered by several obstacles, among them concerns about data privacy and trustworthiness. Current IT solutions fail to satisfy patients’ privacy desires and do not provide a trustworthiness measure for medical data. This thesis starts with the observation that existing EHR system proposals suer from six serious shortcomings that aect patients’ privacy and safety, and medical practitioners’ trust in EHR data: accuracy and privacy concerns over linking patients’ existing medical records; the inability of patients to have control over who accesses their private data; the inability to protect against inferences about patients’ sensitive data; the lack of a mechanism for evaluating the trustworthiness of medical data; and the failure of current healthcare workflow processes to capture and enforce patient’s privacy desires. Following an action research method, this thesis addresses the above shortcomings by firstly proposing an architecture for linking electronic medical records in an accurate and private way where patients are given control over what information can be revealed about them. This is accomplished by extending the structure and protocols introduced in federated identity management to link a patient’s EHR to his existing medical records by using pseudonym identifiers. Secondly, a privacy-aware access control model is developed to satisfy patients’ privacy requirements. The model is developed by integrating three standard access control models in a way that gives patients access control over their private data and ensures that legitimate uses of EHRs are not hindered. Thirdly, a probabilistic approach for detecting and restricting inference channels resulting from publicly-available medical data is developed to guard against indirect accesses to a patient’s private data. This approach is based upon a Bayesian network and the causal probabilistic relations that exist between medical data fields. The resulting definitions and algorithms show how an inference channel can be detected and restricted to satisfy patients’ expressed privacy goals. Fourthly, a medical data trustworthiness assessment model is developed to evaluate the quality of medical data by assessing the trustworthiness of its sources (e.g. a healthcare provider or medical practitioner). In this model, Beta and Dirichlet reputation systems are used to collect reputation scores about medical data sources and these are used to compute the trustworthiness of medical data via subjective logic. Finally, an extension is made to healthcare workflow management processes to capture and enforce patients’ privacy policies. This is accomplished by developing a conceptual model that introduces new workflow notions to make the workflow management system aware of a patient’s privacy requirements. These extensions are then implemented in the YAWL workflow management system.

An empirical test of causal relationships of factors affecting ICT adoption for building project management : an Indian SME case study

Purpose - Building project management (BPM) requires effective coordination and collaboration between multiple project team organisations which can be achieved by real time information flow between all participants. In the present scenario, this can be achieved by the use of information communication technologies (ICT). The purpose of this paper is to present part of a research project conducted to study the causal relationships between factors affecting ICT adoption for BPM by small and medium enterprises. Design/methodology/approach - This paper discusses structural equation modelling (SEM) analysis conducted to test the causal relationships between quantitative factors. Data for quantitative analysis were gathered through a questionnaire survey conducted in the Indian construction industry. Findings - SEM analysis results help in demonstrating that an increased and matured use of ICT for general administration within the organisation would lead to: an improved ICT infrastructure within the organisation; development of electronic databases; and a staff that is confident of using information technology (IT) tools. In such a scenario, staff would use advanced software and IT technologies for project management (PM) processes and that would lead to an increased adoption of ICT for PM processes. But, for general administration also, ICT adoption would be enhanced if the organisation is interacting more with geographically separated agencies and senior management perceives that significant benefits would accrue by adoption of ICT. All the factors are inter-related and their effect cannot be maximized in isolation. Originality/value - The results provide direction to building project managements for strategically adopting the effective use of ICT within their organisations and for BPM general.

Approximate Bayesian computation using indirect inference

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms by using indirect inference. ABC methods are useful for posterior inference in the presence of an intractable likelihood function. In the indirect inference approach to ABC the parameters of an auxiliary model fitted to the data become the summary statistics. Although applicable to any ABC technique, we embed this approach within a sequential Monte Carlo algorithm that is completely adaptive and requires very little tuning. This methodological development was motivated by an application involving data on macroparasite population evolution modelled by a trivariate stochastic process for which there is no tractable likelihood function. The auxiliary model here is based on a beta–binomial distribution. The main objective of the analysis is to determine which parameters of the stochastic model are estimable from the observed data on mature parasite worms.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.