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Fundamentals of Artificial Intelligence

Info

Homework
Course-related instructions: PDF
Course syllabus at IS MU
Study materials
- All learning materials with slides
- Video recordings
- Sample exams from last years: I., II.
- Last year's slides in PDF: handout and for presentation
  - also available to all students who have taken the course in the past
  - slides in Czech from 2021: PDF handout and for presentation
- Textbooks and Internet resources
  - Russell, S., Norvig, P. Artificial Intelligence: A Modern Approach, 4th edition. Pearson, 2021.
    Available at MU electronically via e-loan. See also the book's exercises
  - Online course, Intro to Artificial Intelligence, Peter Norvig, Sebastian Thrun.
  - Search and metaheuristics
    - El-Ghazali Talbi, Metaheuristics: From Design to Implementation. Wiley, 2009. PDF
    - Holger H. Hoos und Thomas Stützle, Stochastic Local Search. Morgan Kaufmann Publishers, 2005.
      http://iridia.ulb.ac.be/~stuetzle/Teaching/HO14/
    - Metaheuristic optimization frameworks: a survey and benchmarking, Soft Computing, 2012, Volume 16, Issue 3, pp 527-561.
      http://link.springer.com/article/10.1007/s00500-011-0754-8
  - Planning
    - Roman Barták, Planning and scheduling course at MFF UK,
      http://ktiml.mff.cuni.cz/~bartak/planovani/index.html
    - M. Ghallab, D. Nau, P. Traverso, Automated Planning and Acting. Cambridge University Press, 2016.
      http://projects.laas.fr/planning/
    - M. Ghallab, D. Nau, P. Traverso, Automated Planning: Theory and Practice. Morgan Kaufmann, 2004. (older edition of the previous book)
      http://projects.laas.fr/aptp/
  - Uncertainty
    - Roman Barták, Artificial Intelligence II course at MFF UK,
      http://ktiml.mff.cuni.cz/~bartak/ui2/index.html
  - Robotics
    - Steven M. LaValle, Planning algorithms, Cambridge University Press, 2006.
      http://planning.cs.uiuc.edu/ with books source in PDF
Questions and consultation: send an e-mail to the teacher, Hana Rudová

Slides in one file will be published at the end of the semester

The outline below is preliminary. Slides will be added during the semester

Introduction to artificial intelligence. Search and metaheuristics: concepts, introduction to single-solution based metaheuristics.

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Single-solution based metaheuristics.

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Population-based metaheuristics.

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Programming homework: pairing October 9, specification October 16, deadline October 29 21:00

Automated planning: representation, PDDL, use cases

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State space planning. Hierarchical task networks.

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Plan space planning.

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Probability theory (short revision). Bayesian networks. Exact and approximate inference.

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Probabilistic reasoning over time.

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Midterm test: November 20

Utility theory, decision making, sequential decision problems.

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Value and policy iteration. Robots, robotic perception. Planning: configuration space.

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Robotics: motion planning

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Exercises with solutions

Přejít

Acknowledgements

Course materials are based on the following sources:

Roman Barták, Planning and scheduling course at MFF UK
http://ktiml.mff.cuni.cz/~bartak/planovani/index.html
Roman Barták, Artificial intelligence II course at MFF UK
http://ktiml.mff.cuni.cz/~bartak/ui2/index.html
Roman Barták, Introduction to Artificial Intelligence course at MFF UK, http://ktiml.mff.cuni.cz/~bartak/ui_intro/index.html
El-Ghazali Talbi, Metaheuristics: From Design to Implementation. Wiley, 2009.
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th edition. Prentice Hall, 2021
http://aima.cs.berkeley.edu/, available at MU electronically via e-loan

Předchozí

Následující

Fundamentals of Artificial Intelligence
- Nyní studovat
  
  Slides from all lectures in one file
- Nyní studovat
  
  Introduction to artificial intelligence. Search and metaheuristics: concepts, introduction to single-solution based metaheuristics.
- Nyní studovat
  
  Single-solution based metaheuristics.
- Nyní studovat
  
  Population-based metaheuristics.
- Nyní studovat
  
  Automated planning: representation, PDDL, use cases
- Nyní studovat
  
  State space planning. Hierarchical task networks.
- Nyní studovat
  
  Plan space planning.
- Nyní studovat
  
  Probability theory (short revision). Bayesian networks. Exact and approximate inference.
- Nyní studovat
  
  Probabilistic reasoning over time.
- Nyní studovat
  
  Utility theory, decision making, sequential decision problems.
- Nyní studovat
  
  Value and policy iteration. Robots, robotic perception. Planning: configuration space.
- Nyní studovat
  
  Robotics: motion planning
- Nyní studovat
  
  Exercises with solutions

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