This thesis presents a case study of Game Mechanic based E-Learning. This is put forward as a new approach to E-Learning that tries to mimic games to harness some of their motivational properties. A prototype system was developed as a web application, using an Agile and Lean development approach.The system was evaluated with a class at the Norwegian University of Science and Technology.This was done to give an indication of the system's ability to make work with exercises more engaging and fun. To give context in this thesis, the growing trend of Gamification is unveiled and explained in detail.The major technological delivery posited by this thesis was the prototype, implementedas a web application (dynamic webpage). The major research acheivement was evaluatingrespondents perception of the system. It was discovered that the chosen Game Mechanicwas indeed considered to make work with exercises more engaging, although this effect wasmarginal. The evaluation was also used to arrive at a general definition for games.This definition can be used to distinguish Game Mechanics basedsystems from games. It also serves as a much needed guide to designing games andnon-game systems that tries to acheive similar motivational benefits as games.

In this project I have designed a model of features, human behavior and intentions. The model suggests a set of features that can be used to describe the interaction between a human being and an automated sliding door. The model also defines symbols representing value sets for the features. The symbols are then combined in order to describe different events, mapping features to intentions. This model provides a framework guiding the capturing process as well as the reasoning process.Further, I have designed a mechanism for capturing human movement and extracting the features as suggested by the model of features, human behavior and intentions. The solution components are based on research done within computer vision, where different tools and algorithms were reviewed and evaluated. Parts of the suggested solution are provided as software libraries, while others had to be implemented. The solution includes using an Xbox Kinect as a sensor device, and the OpenNI framework together with the middleware NITE for Human body tracking and skeletal joint extraction.A reasoning mechanism was designed, that utilizes the designed model in order to reach a conclusion about the intention of a human interacting with the door. Different reasoning techniques were reviewed in context of the sliding doors problem. Based on the review I suggest using rule-based reasoning. By using the events described in the model and by giving values to the different symbols I was able to form the rules for the reasoning process.The designed mechanisms were put together in an implementation in C/C++ comprising depth and RGB image capture, body tracking, user handling and feature extraction, rule-based reasoning and door control.A motorized sliding door was built, together with a door controller allowing a computer to interface with the door, giving open and close commands.Finally, the door was tested both through a live demo and a laboratory style, structured observation. The door proved a superior performance to the traditional sliding doors when it came to identifying negative intentions, thus reducing the number of false positives drastically. However, both false positives and false negatives occurred, leaving room for improved accuracy.With my solution I have managed to interpret the intention of a user interacting with an automated sliding door. I have lifted the reasoning process to a symbolic level, dealing with symbols and events easy to understand. Although the model is limited to a very specific domain, and the solution has got some limitations and weaknesses, this is a good starting point for further work.

In this thesis we will construct a recommender system for course selection in higher education (more specifically, at The Norwegian University of Science and Technology). Some of what makes our approach novel compared with existing solutions is that we weight each user in the collaborative filtering process based on their chosen degree subject (major) and wether or not the two users being compared are friends. Also we utilize both collaborative filtering and content-based recommendations in a hybrid solution. Another novel aspect of our solution is that we construct our system on top of an existing website for rating courses. This gives us unique access to a dataset containing thousands of user-ratings of courses.

In the field of artificial intelligence, recommender systems are methods for predicting the relevance items to a users. The items can be just about anything, for example documents, articles, movies, music, events or other users. Recommender systems examine data such as ratings, query logs, user behavior and social connections to predict what each user will think of each item.Modern recommender systems combine multiple standard recommenders in order to leverage disjoint patterns in available data. By combining different methods, complex predictions that rely on much evidence can be made. These aggregations can for example be done by estimating weights that result in an optimal combination.However, we posit these systems have an important weakness. There exists an underlying, misplaced subjectivity to relevance prediction. Each chosen recommender system reflects one view of how users and items should be modeled. We believe the selection of recommender methods should be automatically chosen based on their predicted accuracy for each user and item. After all, a system that insists on being adaptive in one particular way is not really adaptive at all.This thesis presents a novel method for prediction aggregation that we call adaptive recommenders. Multiple recommender systems are combined on a per-user and per-item basis by estimating their individual accuracy in the current context. This is done by creating a secondary set of error estimating recommenders. The core insight is that standard recommenders can be used to estimate the accuracy of other recommenders. As far as we know, this type of adaptive prediction aggregation has not been done before.Prediction aggregation (combining scores) is tested in a recommendation scenario. Rank aggregation (sorting results lists) is tested in a personalized search scenario. Our initial results are promising and show that adaptive recommenders can outperform both standard recommenders and simple aggregation methods. We will also discuss the implications and limitations of our results.

The goal of this thesis was to investigate the usage of mobile devices with multitouch capabilities in the learning of procedural knowledge. A system, consisting of three prototypes, was to be implemented as a way of examining our two hypotheses:H1: Through using a conceptual model close to how the human mind perceive objects, we can increase consistency both in the creation of new user manuals and in the learning process.H2: By taking advantage of multitouch technologies we can introduce a more natural way of interacting on virtual representations of real-life objects.A lot of research was conducted on the usage of a conceptual model containing information on the physical attributes and the procedural knowledge to back our applications, and how this best could be realized. Existing technologies for creating 3D models was investigated, but was quickly discarded due to the unique representation that was needed to successfully integrate the model with GOMS. The research process concluded that an application for describing new devices would have to be developed as well.Three applications was developed to investigate our hypotheses: an application for describing the aspects of a device, written for Mac OS, a server for communicating with prolog over TCP, written in Java, and an application for displaying the device and allowing for interaction, written for the iOS platform. The final versions of these three prototypes made it possible to create objects consisting of cubes, storing them on the server, and rendering them on the mobile application. The report concludes by discussing the utility of our prototype in regards to the hypotheses. Although not in its optimal state, the prototype demonstrates the utility of pure gestural interfaces, and how well established technologies such as prolog and GOMS can be used to empower them. Finally, interesting extensions and further work based on this thesis is proposed, demonstrating its versatility.

Artificial Evolution has shown great potential in the musical domain. One task in which Evolutionary techniques have shown special promise is in the automatic creation or composition of music. However, a major challenge faced when constructing evolutionary music composition systems is finding a suitable fitness function.Several approaches to fitness have been tried. The most common is interactive evaluation. However, major efficiency challenges with such an approach have inspired the search for <i>automatic</i> alternatives.In this thesis, a music composition system is presented for the evolution of novel melodies. Motivated by the repetitive nature of music, a <i>quantitative</i> approach to automatic fitness is pursued. Two techniques are explored that both operate on frequency distributions of musical events. The first builds on <i>Zipf's Law</i>, which captures the scaling properties of music. Statistical <i>similarity</i> governs the second fitness function and incorporates additional domain knowledge learned from existing music pieces.Promising results show that pleasant melodies can emerge through the application of these techniques. The melodies are found to exhibit several favourable musical properties, including rhythm, melodic locality and motifs.

In this thesis, I present an approach to a case-based reasoning system with explanation capabilities in the Warhammer Fantasy Battle domain. This product is meant to support Warhammer gamers in their initial army lineup, by providing suggestions based on previously successful games against an opposing horde. Explanations will be used in order to convey the reasoning behind the solution, to present the data the solution is based upon and why certain changes were made.The created product is capable of creating the army lineup and give partially satisfactory explanations, based on the goals set both for the application as a whole and explanations. Although a full domain model is not implemented, are the results promising; with the inclusion of more domain knowledge and cases, will a fully competent and accurate system be achievable.

When tasks traditionally performed by humans are automated it is important thatthe machines are able to communicate how these tasks are solved and why. Whena user is surprised by the point of time where the task is executed, there is a needto be able to get an explanation to why this point in time was chosen.This project aims at investigating how intelligent systems in general, and case-based reasoning systems in particular can become explanation-aware. Our aim isprimarily to investigate existing case-based reasoning systems to see if explanation-awareness is achievable. Secondary, our aim is to develop a simple case-based rea-soning engine that complies with our theoretical work on explanation-awareness.

Artificial intelligence and learning is a growing field. There are many ways of making a computer program learn, in most cases one have a specific problem one wants to solve and do not really care how it is solved. This thesis have a specific problem, but the main focus is on how it is solved. One of the most exciting ways to learn is by the so called unsupervised learning methods, where programs/agents learn without any human interaction. Psychologists and Neurologists have long tried to understand how the human brain works, but due to its complexity there are still some obstacles left before we will be able to simulate the different functionalities. This thesis is an attempt to get one step closer to solving the problem of how learning happens and memories form. If we were to be able to simulate human learning in a machine there is no telling where it could end. Jørn Hokland has put forward three learning rules that may describe how learning happens. These rules will be examined and then used in an artificial neural network with the intention to control a simulated robot. Artificial neural networks (ANNs) are more or less inspired by the biological neural networks (BNNs) found in humans and animals. As we will see this thesis seeks to be one of the more biologically inspired.

The Creek system has an architecture that facilitates combined case-based and model-based reasoning. The jColibri system, developed by the CBR group of Universidad Complutense in Madrid, contains a library of CBR system components intended for sharing and reuse. The system also contains an ontology (CBROnto) of CBR tasks and methods for explicit modelling of a CBR systems, in addition to general CBR terminology. In this master degree project, Creek and jColibri are compared with the aim of developing a mechanism for importing jColibri components to Creek, so that they can be integrated into a running Creek system. The mechanism is exemplified through selection of a few specific components, and integration of these components into an implemented demonstrator system. In addition, efforts needed to bring Creek into the jColibri framework are identified.