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• The course presentation form.
• The lectures and the exams will be delivered in English.

• The official course page in OLE.

The official week-by-week Faculty timetable can be found on the RIS BSc 3rd year.

Office hours: anytime, by previous appointment by email to the lecturer (Enrico Franconi).

• 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.

red means material not done this year.

• 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).
• 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%