The main components of the Doctoral School will be a series of three advanced tutorials on aspects of Artificial Intelligence for decision making. In addition, a series of short talks given by each of the PhD students attending the school will provide a forum for exchanging ideas and receiving feedback from the attendees.

The three tutorials that will be delivered at the workshop are listed below, with short biographies of the tutorialists.

The Doctoral School Programme

Tutorial 1: Problem Solving with Constraint Programming (14 hours approx.)

In this course we show how interesting combinatorial problems can be easily modeled and solved with constraint programming. We present the underlying principles on which constraint programming is based, as well as modeling techniques for expressing constraints and specifying problem specific heuristics. The course uses the ECLiPSe system, an open source constraint platform allowing fast prototyping and advanced visualization. The course will also present an overview of successful, industrial constraint applications in areas of scheduling, transport, personnel planning and data networks.

Tutorialist. Helmut Simonis is a senior researcher at 4C, University College Cork. He has over 20 years experience in modeling and solving constraint problems, and is a leading expert on applications of constraint technology.

Tutorial 2: Reasoning Under Uncertainty (14 hours approx.)

One of the main challenges in building intelligent systems is the ability to reason under uncertainty, and one of the most successful approaches for dealing with this challenge is based on the framework of Bayesian (or belief) networks. Intelligent systems based on Bayesian networks are being used in a variety of real-world applications including diagnosis, sensor fusion, on-line help systems, credit assessment, bioinformatics and data mining. The objective of this seminar is to provide an in-depth exposition of knowledge representation and reasoning under uncertainty using the framework of Bayesian networks.

Tutorialist. Dr. Radu Marinescu received his PhD and MS degrees in Computer Science from the University of California, Irvine under the supervision of Prof. Rina Dechter. His research centers on search algorithms that explore the AND/OR search spaces, for graphical models, and use the Mini-Bucket approach the generate heuristic functions automatically. During the past years he worked within several optimization frameworks that arise in the areas of constraint based reasoning (constraint networks) and reasoning under uncertainty (belief networks).

Tutorial 3: Computing Explanations in Decision Making (4 hours approx.)

Explanation generation is an essential feature of intelligent systems. Explanations are important in interactive systems such as product configurators, personal assistants, expert systems, and model development tools. Explanation techniques are also used to improve the quality of results in test generation, software verification, and decomposition methods in combinatorial optimization. This tutorial develops a general theory of explanation generation in problem solving, which unifies existing methods and is valid for a large range of AI problems (product configuration, multi-criteria optimization, constraint satisfaction, satisfiabililty, recommender systems, case-based reasoning, diagnosis, debugging, ontological reasoning, etc.). Only a basic background knowledge of AI is required. The tutorial will characterise problems needing explanations while analyzing the explanation requirements of different types of user. We will introduces different notions of explanation such as minimal and preferred explanations, abstraction and refinement, representative explanations, explanations of solutions, and explanations of optimality. The computation of one, several, or all explanations can be achieved by parameterized algorithms, which work for arbitrary problem solvers such as constraint solvers. The tutorial studies the relationship between new algorithms and classic methods. The tutorial concludes with a list of successful applications of explanation generation ranging from product configurators, recommender systems, debuggers, planning and the web.

Tutorialists. Ulrich Junker received his Ph.D. from the University of Kaiserslautern (1992). He has significant  experience in explanation generation, starting with research on truth maintenance systems and model-based diagnosis. His pioneering work on QuickXplain paved new directions for explanation generation in constraint programming, product configuration, and other fields. He is a distinguished scientist at ILOG.

Barry O'Sullivan is Associate Director of the Cork Constraint Computation Centre at University College Cork. He is the President of the Association for Constraint Programming, Chairman of the Artificial Intelligence Association of Ireland, and Coordinator of the ERCIM Working Group on Constraints. His research interests are constraint programming and optimisation.