Course content

This course gives an introduction to a number of selected topics in artificial intelligence (AI) relevant for solving real-world problems. The course will study AI with respect to modelling a variety of problems in suitable state space; design and implementation of intelligent search and optimization algorithms; simulation and testing of models and algorithms; and visualisation, interpretation, and analysis of the results. Upon completion of this course, students should have attained a level of knowledge, skills, and general competence in the field of AI sufficient to solve a number of real-world problems in many different domains. The limitation of 7.5 credits means that only a limited number of topics can be studied in depth during a semester. Which topics that will be studied will be announced some time before start of the semester. The selected topics may vary each year. Typical topics may include some of the following: • introduction to AI and intelligent agents. • modelling problems terms of performance measure, environment, sensors, and actuators (PEAS). • analysis and classification of models and algorithms. • search algorithms: - uninformed search (e.g., DFS, BFS, UCS, IDS). - informed (heuristic) search (e.g., greedy BFS, A*). - local search (e.g., hillclimbing search, local beam search). - adversarial search (e.g., minimax, expectimax, expectiminimax, alpha-beta pruning). • simulation of models for problem solving. • optimization problems. • constraint satisfaction problems (CSP). • computational intelligence and optimization algorithms, e.g.: - evolutionary algorithms (e.g, GA, MOOGA, SA). - swarm intelligence (e.g., PSO, ACO, GWO), if not covered in other courses. - fuzzy expert systems, if not covered in other courses. - artificial neural networks, if not covered in other courses. - hybrid intelligent systems, if not covered in other courses.

Learning outcome

Knowledge: Upon completion of the course, students should be able to • describe AI in terms of the analysis and design of intelligent agents or systems that interact with their environments • explain relevant AI terminology, models, and algorithms used for problem solving, as well as limitations and risks.

Skills: Upon completion of the course, students should be able to • model problems in suitable state space depending on choice of solution method • simulate models and solve problems by means of AI methods, e.g., search algorithms or computational intelligence • analyse models, AI methods, and simulation results

General competence: Upon completion of the course, students should be able to • read and understand scientific publications and textbooks on AI and reformulate the presented problems, choice of methods, and results in a short, concise manner • discuss and communicate advantages and limitations of selected AI methods for problem solving,

Upon completion of the course, students can take active part in the informed discussion not only about what AI can do, but also what AI should be allowed to do, and what measures may be required to make AI beneficiary to human society.

Learning methods and activities

In addition to more traditional lectures, this course adopts a flipped classroom approach and a workshop format that emphasise interaction and active learning. Activities in class focus on solving problems ranging from 2-min exercises to larger assignments and projects, discussions, lectures, tutorials, and demonstrations. Before coming to class, students will study selected textbook chapters and scientific literature, complete homework exercises and assignments, and make use of the resources offered in a high quality online course on AI that includes interactive e-learning material such as video lectures, tutorials, and interactive puzzles, all available for pcs, tablets, and smartphones. Being well-prepared is imperative to ensure a valuable learning environment during workshops. For topics where traditional lectures are not provided in the classroom, ad hoc microlectures will be given on topics in the online videos/resources that students find difficult. A number of mandatory assignments must be passed for permission to enter the oral exam. The assignments are gathered in a portfolio which forms the foundation for the oral exam.

Compulsory assignments

• Mandatory assignment

Specific conditions

Recommended previous knowledge

An undergraduate degree in computing or other disciplines with extensive coverage of computer programming.

Course materials

Main course textbook: • Russell, S. & Norvig, P.: Artifical Intelligence: A Modern Approach (usually latest edition)

The module will also depend on other sources, like papers, web pages, tutorials, and books.

Other relevant textbooks: - Haupt, R. L. & Haupt, S. E.: Practical Genetic Algorithms, Wiley, 2nd Ed. (2004) - Haykin, S.: Neural Networks and Learning Machines, Pearson, 3rd Ed. (2008) - Stephen Marsland: Machine Learning: An Algorithmic Perspective, CRC Press, 2. utg. (2015) - Simon, D.: Evolutionary Optimisation Algorithms: Biologically Inspired and Population-based Approaches to Computer Intelligence, Wiley (2013)

Credit reductions