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• The course presentation form.

The official week-by-week Faculty timetable: lectures and labs of the course. Note that a LAB may be transformed into a LECTURE and vice-versa.

Office hours: anytime, by previous appointment by email to the lecturer (Enrico Franconi). In any case the lecturer is always available for the period after any lecture.

Some lectures and labs may be available on Microsoft Teams.

• Exclusively English.

• Main book: David Poole and Alan Mackworth. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press, 2023, 3rd edition 2017. The book is fully available online.
• Auxiliary book: Stuart Jonathan Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 4th edition 2020.
• Reading list from the UniBZ Library: Artificial Intelligence / Artificial Intelligence: Foundation of Artificial Intelligence

The following is the standard material, it may be adjusted during the course.

• Slides: Welcome Aboard
• Slides: Artificial Intelligence and Agents parts 1 & 2, part 3, part 4
  • Material: chapter 1 of Poole and Mackworth
• Slides: States and Searching part 1, part 2, part 3, part 4
  • Material: chapter 3 of Poole and Mackworth
• Slides: Features and Constraints part 1, part 2
  • Material: chapter 4 of Poole and Mackworth
• Slides: Propositions and Inference
  Slides: Diagnosis
  • Material: chapter 5 of Poole and Mackworth
• Slides: Deterministic Planning
  • Material: chapter 6 of Poole and Mackworth

• LAB 1: Graph Searching with uninformed techniques (Java applet)
  • Manuals:
    • Quick start
    • General Help
    • Tutorial 1: Creating a New Graph with video
    • Tutorial 2: Loading a Preexisting Graph
    • Tutorial 3: Solving a Graph with video
    • Tutorial 4: Search Options
  • Explore with the search applet the Delivery Robot (Acyclic), the Delivery Robot (cyclic), the Vancouver Neighbourood, the Module 4 sample problem graphs: with Depth First, Breadth First, Lowest Cost First search strategies using different Neighbour Ordering Strategies; practice also with the quiz facility.
  • Do the Practice Exercise 3.B.
  • Exercise: practicing different search strategies with the graph in this slides (solution as a XML file for the applet)
  • Create your own problem graph for a delivery robot starting from a map with edge costs.
  • Create a problem graph for a simple problem chosen by you.
  • IMPORTANT: learn how to write on paper the frontier evolution for each search.

* LAB 2: Graph Searching with Heuristics

• Explore the Delivery Robot (Acyclic), the Delivery Robot (cyclic), the Vancouver Neighbourood, the Module 4 sample problem graphs: with Best First, Heuristic Depth First, A*, Branch and Bound search strategies, with or without Multiple-Path Pruning or Loop Detection, using different Neighbour Ordering Strategies. Explore also the behaviour with the abovementioned search graphs with potentially non terminating depth first strategies (e.g., Depth First or Heuristic Depth First) without cycle checking, and with loop detection or multiple path pruning.
• Do the Practice Exercises 3.C, 3.D, 3.E.
• Exercise: 3.4 (solution).
• IMPORTANT:
  • learn how to write on paper the frontier evolution for each search;
  • check whether the heuristics are admissible and monotone.

* LAB 3: Constraints - Consistency

• Consistency Based CSP Solver (applet):
• Explore with the CSP applet the sample problems: Simple Problem 1, Simple Problem 2, Scheduling Problem 1, Crossword Problem 1, Crossword Problem 2. These sample problems have been seen already in the course lectures; for the crossword problems, try to reconstruct the crossword graphical structure.
• Do the Practice Exercises 4.A, 4.B.
• Exercises: 4.2, 4.3 (only a,b), 4.5 - CSP and arc consistency.