Artificial Intelligence

Introduction to the Classical Planning model Languages for Classical Planning Search Algorithms: Blind and Heuristic Domain-Independent Heuristics and Relaxations Classical Planning: Complexity, Variations and Extensions Getting to Know and Use a Planner Numeric Planning Reasoning about actions through Situation Calculus and Golog Modeling dynamics of the domain of interest: Precondition Axioms, Successor State Axioms, the Frame Problem Regression and Projection

Students taking this course should have knowledge of object-oriented analysis, modeling and design, relational databases, and basic notions of probabilities, as acquired in previous courses, as well as logic and discrete mathematics. Knowledge and understanding of basic artificial intelligence techniques and concepts is also required.

Teaching material: [1] Course slides, notes, and additional material available on this site. [2] Artificial Intelligence: A Modern Approach, Global Edition, 4th Edition by Stuart Russell, Peter Norvig, Pearson, 2020 [3] A Concise Introduction to Models and Methods for Automated Planning, by Hector Geffner and Blai Bonet, Springer, 2013 [4] Automated Planning: Theory and Practice, by Malik Ghallab, Dana Nau, Paolo Traverso, Elsevier, 2001 [5] An Introduction to the Planning Domain Definition Language, by Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, Christian Muise, Springer, 2019 [6] Knowledge in Action, by Raymon Reiter, MIT Press, 2001

Attendance not mandatory

The exam consists of 2 parts: - A written examination (2h) - A project (group work, min 2 – max 3 students) The final mark of the exam is obtained as a weighted average of the two parts (project: 1/3, written exam: 2/3) and will be registered only once both have been passed. The grade for each part remains valid for the entire A.Y., until the last exam session in October/November 2025. After that, all grades will expire. More information about the projects is available on the course website's projects page: - https://sites.google.com/uniroma1.it/pr2526/projects

Classroom teaching