Modeling knowledge states in language learning

Artificial intelligence (AI) is being increasingly applied in the field of intelligent tutoring systems (ITS). Knowledge space theory (KST) aims to model the main features of the process of learning new skills. Two basic components of ITS are the domain model and the student model. The student model provides an estimation of the state of the student’s knowledge or proficiency, based on the student’s performance on exercises. The domain model provides a model of relations between the concepts/skills in the domain. To learn the student model from data, some ITSs use the Bayesian Knowledge Tracing (BKT) algorithm, which is based on hidden Markov models (HMM). This thesis investigates the applicability of KST to constructing these models. The contribution of the thesis is twofold. Firstly, we learn the student model by a modified BKT algorithm, which models forgetting of skills (which the standard BKT model does not do). We build one BKT model for each concept. However, rather than treating a single question as a step in the HMM, we treat an entire practice session as one step, on which the student receives a score between 0 and 1, which we assume to be normally distributed. Secondly, we propose algorithms to learn the “surmise” graph—the prerequisite relation between concepts—from “mastery data,” estimated by the student model. The mastery data tells us the knowledge state of a student on a given concept. The learned graph is a representation of the knowledge domain. We use the student model to track the advancement of students, and use the domain model to propose the optimal study plan for students based on their current proficiency and targets of study

Solving Factored MDPs with Hybrid State and Action Variables

Efficient representations and solutions for large decision problems with continuous and discrete variables are among the most important challenges faced by the designers of automated decision support systems. In this paper, we describe a novel hybrid factored Markov decision process (MDP) model that allows for a compact representation of these problems, and a new hybrid approximate linear programming (HALP) framework that permits their efficient solutions. The central idea of HALP is to approximate the optimal value function by a linear combination of basis functions and optimize its weights by linear programming. We analyze both theoretical and computational aspects of this approach, and demonstrate its scale-up potential on several hybrid optimization problems

Towards a General Theory of Financial Control for Organisations

In this paper, a theory of accounting, control and accounting-related areas is outlined.It is based on a number of previous research-oriented books published over several decades and the author´s specific own experiences from internal and external processes with organisations in focus.Consistency and integrative power of the ideas have been tested in relation to certain books in various fields outside the core of the subject:theatre,sociology, applied systems theory,economic history, institutional theory and economics.The general approach can be described in simple terms as follows.There are global value chains, from resources to output that are in use.These chains change with time.Uncertainty and unpredictability prevail for the present state and for possible changes; to some extent it is possible to estimate risks of the future. At any moment, each organisation has taken some limited position on a chain.Each organisation has a hierarchy which lies above operations. Over time, chains, organisations, hierarchies, output and personal functions vary. According to the approach, insights into control problems for every organisation and system can be gained by analysing relationships between global value chains and a hierarchy of one or several organisations.Time is crucial.financial control; management control; public administration; financial entities; financial reporting; dependencies; function-driven organisations; pay-driven organisations; transfer-driven organisations; supervisory boards; mass media; auditors; natural systems; panarchy; pseudo-commercial units; inter-organisational control; long-term control; short-term effects; hierarchies; global value chains; vertical control; horizontal control; corporate governance; remote control; controllability; transparency; values-in-use; values-in-exchange; fair values; historical costing; opportunity costs; product costing; transfer pricing; local optimization; time-bound optimization; longitudinal relationships.

Technology-related disasters:a survey towards disaster-resilient software defined networks

Resilience against disaster scenarios is essential to network operators, not only because of the potential economic impact of a disaster but also because communication networks form the basis of crisis management. COST RECODIS aims at studying measures, rules, techniques and prediction mechanisms for different disaster scenarios. This paper gives an overview of different solutions in the context of technology-related disasters. After a general overview, the paper focuses on resilient Software Defined Networks

The Need for a Different Approach to Financial Reporting and Standard-setting

International Financial Reporting Standards are questioned. Possibly, there is a need for a different kind of standards and a different procedure for developing them. No doubt, there is a need for a more profound theoretical approach to these issues. Theory-building in accounting should include approaches whereby problem descriptions have a broad coverage and cross the boarders of traditional specialisations. In this paper, a theoretical approach is outlined. According to this approach, insights into control problems for every organisation and system can be gained by analysing relationships between global value chains and a hierarchy of one or several organisations. Time is crucial. Instrumentality is regarded as an inevitable and necessary guide line for any control system that relates resources to functions and visions. Instrumentality concerns the effects of tools on certain functions. In the paper financial reporting and standard-setting are placed in a wide context in which longitudinal relationships are essential for individuals, organisations and control systems. Basic financial accounting concepts and their relationships with business events are discussed. The importance of uncertainty for financial reporting is emphasized, and so is the fact, that control from top-levels is exercised at a distance. A tendency to instrumentalism is also recognized: measures and procedures, for example standard setting procedures, tend to be important in themselves, irrespective of ultimate economic functions in a wider perspective. The analysis in the paper is one application of a general approach to financial control for all types of organisations. The general approach is based on a number of previous research-oriented books published over several decades and the author´s specific own experiences from internal and external processes with organisations in focus. Consistency and integrative power of the ideas have been tested in relation to certain books in various fields outside the core of the subject: applied systems theory, theatre, sociology, economic history, institutional theory and economics.financial reporting; International Financial Reporting Standards; standard-setting; accounting standard setting bodies; supervisory boards; corporate governance; transparency; market value accounting; mark-to-market; fair values; historical values; accounting theory.

Probabilistic Inference Using Partitioned Bayesian Networks:Introducing a Compositional Framework

Probability theory offers an intuitive and formally sound way to reason in situations that involve uncertainty. The automation of probabilistic reasoning has many applications such as predicting future events or prognostics, providing decision support, action planning under uncertainty, dealing with multiple uncertain measurements, making a diagnosis, and so forth. Bayesian networks in particular have been used to represent probability distributions that model the various applications of uncertainty reasoning. However, present-day automated reasoning approaches involving uncertainty struggle when models increase in size and complexity to fit real-world applications.In this thesis, we explore and extend a state-of-the-art automated reasoning method, called inference by Weighted Model Counting (WMC), when applied to increasingly complex Bayesian network models. WMC is comprised of two distinct phases: compilation and inference. The computational cost of compilation has limited the applicability of WMC. To overcome this limitation we have proposed theoretical and practical solutions that have been tested extensively in empirical studies using real-world Bayesian network models.We have proposed a weighted variant of OBDDs, called Weighted Positive Binary Decision Diagrams (WPBDD), which in turn is based on the new notion of positive Shannon decomposition. WPBDDs are particularly well suited to represent discrete probabilistic models. The conciseness of WPBDDs leads to a reduction in the cost of probabilistic inference.We have introduced Compositional Weighted Model Counting (CWMC), a language-agnostic framework for probabilistic inference that partitions a Bayesian network into subproblems. These subproblems are then compiled and subsequently composed in order to perform inference. This approach significantly reduces the cost of compilation, yet increases the cost of inference. The best results are obtained by seeking a partitioning that allows compilation to (barely) become feasible, but no more, as compilation cost can be amortized over multiple inference queries.Theoretical concepts have been implemented in a readily available open-source tool called ParaGnosis. Further implementational improvements have been found through parallelism, by exploiting independencies that are introduced by CWMC. The proposed methods combined push the boundaries of WMC, allowing this state-of-the-art method to be used on much larger models than before

Learning Behavior Models for Interpreting and Predicting Traffic Situations

In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees

Interactive Exploration of Multitask Dependency Networks

Scientists increasingly depend on machine learning algorithms to discover patterns in complex data. Two examples addressed in this dissertation are identifying how information sharing among regions of the brain develops due to learning; and, learning dependency networks of blood proteins associated with cancer. Dependency networks, or graphical models, are learned from the observed data in order to make comparisons between the sub-populations of the dataset. Rarely is there sufficient data to infer robust individual networks for each sub-population. The multiple networks must be considered simultaneously; exploding the hypothesis space of the learning problem. Exploring this complex solution space requires input from the domain scientist to refine the objective function. This dissertation introduces a framework to incorporate domain knowledge in transfer learning to facilitate the exploration of solutions. The framework is a generalization of existing algorithms for multiple network structure identification. Solutions produced with human input narrow down the variance of solutions to those that answer questions of interest to domain scientists. Patterns, such as identifying differences between networks, are learned with higher confidence using transfer learning than through the standard method of bootstrapping. Transfer learning may be the ideal method for making comparisons among dependency networks, whether looking for similarities or differences. Domain knowledge input and visualization of solutions are combined in an interactive tool that enables domain scientists to explore the space of solutions efficiently