Örebro University

Our imagination has always been a powerful source of alternative realities e.g. imaginary artifacts, characters, stories art and literature. Over time, new technologies such as moving images and Virtual Reality have opened up more ways to create and let others experience new realities. Mixing real and virtual worlds has always been attractive but it only became possible recently due to technological developments. Video games created interest in Virtual Reality, not only in the field of entertainment but also for industrial applications. The course examines phenomena of human interaction with virtual and augmented reality. We examine different aspects of human perception of virtual reality, depending on factors such as true-to-life rendering and experience. We look at how people act when they take part in virtual environments. We also look at the methods for building effective interaction scenarios and measuring interaction quality with both qualitative and quantitative tools. The course is aimed at those who are professionals.

The real world is distributed. Distributed artificial intelligence is about this spread of knowledge, skills and abilities, across different units and different places. The course introduces Multi-Agent Systems - the main concept and technology for distributed AI. We will discuss how to build systems that can function as part of a Multi-agent System; how to organize different intelligent devices, how to use negotiation or auctions to distribute tasks to autonomous agents and many more topics from Swarm Intelligence to teams of intelligent robots.

Reinforcement learning - (RL) is a method for learning to make an optimal decision through trial and error. The goal of RL is to achieve an optimal policy for each state in a system. The course covers the underlying formalism of RL called Markovska decision-making processes and basic RL algorithms. Examples are dynamic programming. We will show how to model a problem as a Markovska decision-making process and implement basic RL algorithms to solve them. In addition, we will explore different ways to compare and evaluate the performance of learning methods.

Answer Set Programming (ASP) is a declarative programming paradigm designed within the field of Artificial Intelligence (AI), and used to solve complex search-problems. The declarative nature of ASP allows one to encode a problem by means of logic. In this way, unlike in imperative programming approaches, there is no need to design an algorithm as a solution for the given problem. In this sense, ASP is comparable with SAT-based encoding or constraint satisfaction problems. However, due to its stable-model semantics, ASP provides a richer representation language useful to handle uncertain situations more effectively for real world scenarios. The advantages of declarative programming together with non-monotonic nature of ASP in handling uncertainties have recently made ASP more attractive both for academia and industry. This course focuses on formalizing and solving various search problems in planning, scheduling and system configuration in ASP.

Örebro University is offering a course in machine learning. The course will offer knowledge of the basic concepts with machine learning, the selection and application of different machine learning algorithms as well as evaluation of the performance of these learning systems. After completing the course, student should be able to prepare data and apply machine learning techniques to solve a problem in an intelligent system. The course is part of the education initiative Smarter at Örebro University.