<p>This thesis is about evolving a control system for a snake called Transformer <-> #13. This is a mechanical snake with several body parts. The choise was to use a cellular genetic algorithm where each body part is a cell. These contain “DNA”, one ruleset for each degree of freedom in the joints, which decides how it will behave in relation to its neighbour body parts. Three different fitness functions have been implemented which each gives a distinct and different behaviour. The goal of the different fitness functions is; crawling far, rising high and making geometry. The crawling part was successfull, while the other two goals was much harder for the snake and didnt provide great results. Concluding that the snake is appropriate for crawling around and making an impression of different cubic forms. Which for artist purposes is adequate, but it fails on getting into specific shapes.</p>

<p>There is a significant amount of ongoing research in the collaborative filtering field, with much of the research focusing on how to most accurately give item predictions to a user, based on ratings given by other users with similar rating patterns. The objective of this project is to build movie rating prediction models with a simple and intuitive representation, based on previous work within the area. Important factors are the investigation of the predictive power of these models, and the research on how the use of content information can improve accuracy when the available data is sparse. We show that latent class models provide an expressive, but yet simple way to represent the movie rating scenario, and that the models have great potential when it comes to predictive accuracy. We conclude that the inclusion of additional content features into the models can help improve the accuracy when there is little data available.</p>

<p>NA</p>

<p>The purpose of this thesis is to obtain and represent the orientation of the muscle fibres in the left ventricular wall of the human heart. The orientation of these fibres vary continuously through the wall. This report features an introduction to the human heart and medical imaging techniques. Attention is gradually drawn to concepts in computer science, and how they can help us get a “clearer picture” of the internals of, perhaps, the most important organ in the human body. A highly detailed Magnetic Resonance Imaging data set of the left ventricle cavity is used as a base for the analysis with 3-D morphological transformations. Also, a 3-D extension of the Hough transformation is developed. This does not seem to have been done before. An attempt is made to obtain the general trend of the trabeculae carneae, as it is believed that this is the orientation of the inner-most muscle fibres of the heart wall. Suggestions for further work include refinement of the proposed 3-D Hough transformation to yield lines that can be used as guides for parametric curves. Also a brief introduction to Diffusion Tensor Magnetic Resonance Imaging is given.</p>

<p>In this thesis we describe our study of two knowledge intensive Conversational Case-Based Reasoning (CCBR) systems and their methods. We look in particular at the way they have solved inferencing and question ranking. Then we continue with a description of our own design for a CCBR system, that will help patients share their experiences of side effects with drugs, with other patients. We describe how we create cases, how our question selection methods work and present an example of how the domain model will look. It is also included a simulation of how a dialogue would be for a patient. The design we have created is a good basis for implementing a knowledge intensive CCBR system. The system should work better than a normal CCBR system, because of the inferencing and question ranking methods, which should lessen the cognitive load on the user and require fewer questions answered, to reach a good solution.</p>

<p>This paper describes a case based reasoning system that is used to guide the parameters of a segmentation algorithm. Instead of using a fixed set of parameters that gives the best average result over all images, the parameteres are tuned to maximize the score for each image separately. The system's foundation is a set of 20 cases that each contains one 3D MRI image and the parameters needed for its optimal segmentation. When a new image is presented to the system a new case is generated and compared to the other cases based on image similarity. The parameters from the best matching case are then used to segment the new image. The key issue is the use of an iterative approach that lets the system adapt the parameters to suit the new image better, if necessary. Each iteration contains a segmentation and a revision of the result, and this is done until the system approves the result. The revision is based on metadata stored in each case to see if the result has the expected properties as defined by the case. The results show that combining case based reasoning and segmentation can be applied within image processing. This is valid for choosing a good set of starting parameters, and also for using case specific knowledge to guide their adaption. A set of challenges for future research is identified and discussed at length.</p>

<p>Go is an ancient two player board game that has been played for several thousand years. Despite its simple rules, the game requires players to form long-term strategic plans and also possess strong tactical skills to handle the complex fights that often occur during a game. From an artificial intelligence point of view, Go is notable as a game that has been highly resistant to all traditional game playing approaches. In contrast to other board games such as chess and checkers, top human Go players are still significantly better than any computer Go playing programs. It is believed that the strategic depth of Go will require the use of new and more powerful artificial intelligence methods than the ones successfully used to create computer players for such other games. There have been some promising new developments using new Monte Carlo-based techniques to play computer Go in recent years, and programs based on this approach are currently the strongest computer Go players in the world. However, even these programs still play at an amateur level, and they cannot compete with professional or strong amateur human players. In this thesis we explore the idea of reusing experience from previous games to identify strategically important moves for a Go board position. This is based on finding a previous game position that is highly similar to the one in the current game. The moves that were played in this previous game are then adapted to generate new moves for the current game situation. A new computer Go playing system using Monte Carlo-based Go methods was designed as a part of this thesis work, and a prototype implementation of this system was also developed. We extended this initial prototype using case based reasoning (CBR) methods to quickly identify the most strategically valuable areas of the board at the early stages of the game, based on finding similar positions in a collection of professionally played games. The last part of the thesis is an evaluation of the developed system and the results observed using our implementation. These results show that our CBR-based approach is a significant improvement over the initial prototype, and in the opening game it allows the program to quickly locate the most strategically interesting areas of the board. However, by itself our approach does not find strong tactical moves within these identified areas, and thus it is most valuable when used to provide strategic guidelines for other methods that can find tactical plays.</p>

<p>In this project we seek to explore whether it is possible for an artificial agent to learn how to play curling. To achieve this goal we developed a simulator that works as an environment where different agents can be tested against each other. Our most successful agent use a Linear Target Function as a basis for selecting good moves in the game. This agent has become very adept at placing stones, but we discovered that it lacks the ability to employ advanced strategies that reach over more than just one stone. In an effort to give the agent this ability we expanded it using Q-learning with UCT, however this was not successful. For the agent to work we need a good representation of the information in curling, and our representation was quite broad. This caused the training of the agent to take an unreasonably large amount of time.</p>

<p>The gameplay of real-time strategy games can be divided into macromanagement and micromanagement. Several researchers have studied automated learning for macromanagement, using a case-based reasoning/reinforcement learning architecture to defeat both static and dynamic opponents. Unlike the previous research, we present the Unit Priority Artificial Intelligence (UPAI). UPAI is a case-based reasoning/reinforcement learning system for learning the micromanagement task of prioritizing which enemy units to attack in different game situations, through unsupervised learning from experience. We discuss different case representations, as well as the exploration vs exploitation aspect of reinforcement learning in UPAI. Our research demonstrates that UPAI can learn to improve its micromanagement decisions, by defeating both static and dynamic opponents in a micromanagement setting.</p>

<p>This report deals with edge and line detection in pictures with complicated and/or blurred objects. It explores the alternatives available, in edge detection, edge linking and object recognition. Choice of methods are the Canny edge detection and Local edge search processing combined with regional edge search processing in the form of polygon approximation.</p>