Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

Theory of fuzzy integrals and its applications

https://www.alaakhamis.org/teaching/MCT200/reading/Information%20Fusion.pdf

Multisensor data fusion: A review of the state-of-the-art

Gamification has drawn the attention of academics, practitioners and business professionals in domains as diverse as education, information studies, human-computer interaction, and health. As yet, the term remains mired in diverse meanings and contradictory uses, while the concept faces division on its academic worth, underdeveloped theoretical foundations, and a dearth of standardized guidelines for application. Despite widespread commentary on its merits and shortcomings, little empirical work has sought to validate gamification as a meaningful concept and provide evidence of its effectiveness as a tool for motivating and engaging users in non-entertainment contexts. Moreover, no work to date has surveyed gamification as a field of study from a human-computer studies perspective. In this paper, we present a systematic survey on the use of gamification in published theoretical reviews and research papers involving interactive systems and human participants. We outline current theoretical understandings of gamification and draw comparisons to related approaches, including alternate reality games (ARGs), games with a purpose (GWAPs), and gameful design. We present a multidisciplinary review of gamification in action, focusing on empirical findings related to purpose and context, design of systems, approaches and techniques, and user impact. Findings from the survey show that a standard conceptualization of gamification is emerging against a growing backdrop of empirical participants-based research. However, definitional subjectivity, diverse or unstated theoretical foundations, incongruities among empirical findings, and inadequate experimental design remain matters of concern. We discuss how gamification may to be more usefully presented as a subset of a larger effort to improve the user experience of interactive systems through gameful design. We end by suggesting points of departure for continued empirical investigations of gamified practice and its effects. We present findings from a survey of the gamification literature.Theoretical findings suggest that gamification is a distinct concept.Conceptual foundations tend to converge on psychological theories of motivation.Early applied work suggests positive-leaning but mixed results.Empirical work on specific elements with direct ties to theory and stronger experimental designs is needed.

Gamification in theory and action

Formal methods and testing are two important approaches that assist in the development of high-quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing.

/pdf/using-formal-specifications-to-support-testing-28e8wig7se.pdf

Using formal specifications to support testing

We present the syntax and proof theory of a logic of argumentation, LA. We also outline the development of a category theoretic semantics for LA. LA is the core of a proof theoretic model for reasoning under uncertainty. In this logic, propositions are labelled with a representation of the arguments which support their validity. Arguments may then be aggregated to collect more information about the potential validity of the propositions of interest. We make the notion of aggregation primitive to the logic, and then define strength mappings from sets of arguments to one of a number of possible dictionaries. This provides a uniform framework which incorporates a number of numerical and symbolic techniques for assigning subjective confidences to propositions on the basis of their supporting arguments. These aggregation techniques are also described, with examples

A logic of argumentation for reasoning under uncertainty

Rule-based generation of requirements traceability relations

An important decision in software projects is when to stop testing. Decision support tools for this have been built using causal models represented by Bayesian Networks (BNs), incorporating empirical data and expert judgement. Previously, this required a custom BN for each development lifecycle. We describe a more general approach that allows causal models to be applied to any lifecycle. The approach evolved through collaborative projects and captures significant commercial input. For projects within the range of the models, defect predictions are very accurate. This approach enables decision-makers to reason in a way that is not possible with regression-based models.

/pdf/predicting-software-defects-in-varying-development-2aies7w2j6.pdf

Predicting software defects in varying development lifecycles using Bayesian nets

Software measurement can play an important risk management role during product development. For example, metrics incorporated into predictive models can give advance warning of potential risks. The authors show how to use Bayesian networks, a graphical modeling technique, to predict software defects and-perform "what if" scenarios.

/pdf/software-measurement-uncertainty-and-causal-modeling-ozuawgmtx8.pdf

Paul Krause

Papers

Using formal specifications to support testing

A logic of argumentation for reasoning under uncertainty

Rule-based generation of requirements traceability relations

Predicting software defects in varying development lifecycles using Bayesian nets

Software measurement: uncertainty and causal modeling