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• The course presentation form.

• The official course page in OLE.

The official week-by-week Faculty timetable can be found on the RIS of the course. Note that sometimes 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.

Lectures are offered now via distance learning using Microsoft Teams with Teams' code 2o00uee. There is a video on how to start with Teams.

• Exclusively English.

• Main book: David Poole and Alan Mackworth. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press, 2010, 2nd edition 2017.
• Auxiliary book: Stuart Jonathan Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 3rd edition 2016.
• Reading list from the UniBZ Library: 76212_19-20-2_CS Artificial Intelligence

You can read below which chapters of the above books are used in the various parts of the course.

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

• Slides: Welcome Aboard
• Slides: Artificial Intelligence and Agents part 1, part 2, part 3, part 4
  • Material: chapter 1 of Poole and Mackworth
• Slides: Agent Architectures and Hierarchical Control part 1, part 2
  • Material: chapter 2 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 part 1, part 2, part 3, part 4
• Slides: Diagnosis part 1, part 2, part 3, part 4, part 5
  • Material chapter 5 of Poole and Mackworth
• Slides: Individuals and Relations part 1, part 2, part 3, part 4
  • Material chapter 12 of Poole and Mackworth
• Slides: Natural Language Understanding
  • Material chapter 12 of Poole and Mackworth
• Slides: Introduction to Machine Learning
• Slides: Supervised learning and Decision Trees: part 1, part 2, part 3.
  • Material chapter 7 of Poole and Mackworth.

Start date: XXX

• LAB 1: Robot Control (applet).
  • Quick start
  • General help
  • Tutorial 1 with video
  • Tutorial 2 with video
  • Tutorial 3 with video
• LAB 2: Recap of Prolog. Material here.
• LAB 3: Graph Searching (I)
  • Graph Searching (applet):
  • Explore the Delivery Robot (Acyclic) and the Delivery Robot (cyclic) sample problem graphs: with Depth First, Breadth First, Lowest Cost First search strategies using different Neighbour Ordering Strategies.
  • 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.
  • Do the Practice Exercise 3.B.
  • Exercise: 3.3(a) - depth-first and breadth-first search strategies.
  • Exercise: 3.4(a) - lowest-cost-first search strategy.
  • Exercise: practicing different search strategies (slides)
• LAB 4: Graph Searching (II)
  • Explore the sample problem graphs below, with Lowest Cost First, Best First, Heuristic Depth First, A* search strategies, with or without Multiple-Path Pruning or Loop Detection, using different Neighbour Ordering Strategies:
    • Delivery Robot (acyclic and cyclic)
    • Misleading Heuristic Demo
    • Multiple-Path Pruning Demo
    • Module 4 Graph
    • Module 5 Graph
    • Bicycle Courier Problem (acyclic and cyclic)
  • Exercise: 3.3(b, c) - best-first and heuristic depth-first with multiple-path pruning search strategies.
  • Exercise: 3.4(b) - heuristic functions and the admissibility check.
• LAB 5 Graph Searching (III), Search in Prolog
  • Exercise: do 3.3(d) using A* with multiple-path pruning search strategies.
    • Test your solution: External Link
  • Do Practice Exercise 3.C.
  • Do Practice Exercise 3.D.
  • Practise with iterative deepening and branch-and-bound
    • Check Example 3.19 on branch-and-bound search
  • Search in Prolog with SWI Prolog or XSB with the InterProlog Studio IDE.
  • Another (simpler?) version in prolog of Depth-first and Breadth-first
  • A prolog implementation of the generic search algorithm (by Davide Lanti) External Link
• LAB 6: Constraints - Consistency
  • Consistency Based CSP Solver (applet):
  • Exercises: 4.1, 4.2, 4.3 (a,b), 4.5 - CSP and arc consistency.
• LAB 7: Propositions and Inference
  • Getting started with AILog2, a representation and reasoning system for definite clauses, with declarative debugging tools.
  • Do Exercises 5.1,5.2,5.3,5.4
  • Find various AILog knowledge base examples (including the one of electrical wiring domain) here
• LAB 8 (Debug, Diagnosis, Abduction)
  • Go through Sections 6, 7, 9 of the manual
  • Play with the following knowledge bases:
    • elect_ask.ail electrical wiring example with askables; Example 5.10 from Section 5.3.2
    • elect_bug.ail the buggy electrical wiring knowledge base from Example 5.14 in Section 5.3.4.1
    • elect_bug2.ail the buggy electrical wiring example from Exercise 5.6
    • elect_bug3.ail a buggy electrical wiring example, which fails to prove lit_l2, but should succeed
    • elect_cbd.ail electrical wiring example for consistency-based diagnosis; Example 5.20 in Section 5.4.3
    • elect_abd.ail electrical wiring example with abduction; Example 5.31 in Section 5.6
    • Exercises: 5.7,5.8,5.9,5.13
• LAB 9: Individuals and Relations
  • Do Exercises:
    • 12.1,12.2 (model theory)
    • 12.3,12.7,12.10,12.12,12.14 (derivations)
• LAB 10: Decision Trees
  • Answer the following questions:
    • What does an arc represent in a decision tree?
    • What does a non-leaf node represent in a decision tree?
    • What does a leaf node represent in a decision tree?
  • Do exercises from (slides)
  • If-time: do Exercise 7.3

Final Written Exam in English: 100%