Programme Outlines and Overviews

This course equips students with advanced knowledge and practical skills in combinatorial optimization and declarative problem solving, preparing them to tackle complex challenges from classical areas of AI such as configuration, design, planning, scheduling, and diagnosis and across various industries. Students will gain a comprehensive understanding of declarative methods and meta-heuristic approaches, learning to model, optimize, and solve real-world problems using state-of-the-art tools and techniques. The course emphasizes hands-on experience and innovative thinking to foster adaptability in problem solving. The course includes the following elements: - **Part I: Declarative Problem Solving.** This part covers declarative methods for solving combinatorial optimization and search problems. Students will explore advanced modelling techniques, focusing on logic-based methods and constraint satisfaction. Examples of such methods are - answer-set programming - answer-set programming with integer constraints (e.g., clingcon, clingo-dl) - constraint programming and optimization tools (e.g., MiniZinc, Google OR-Tools, CPLEX). - **Part II: Optimization by Intelligent Techniques.** This part focuses on heuristic and meta-heuristic approaches to optimization. Students will learn a variety of techniques and algorithms and apply them to solve complex problems efficiently. Such techniques include (but are not limited to) - heuristics and meta-heuristics, - evolutionary computation, and - swarm intelligence.