Intelligenza artificiale LM 2011/2012
pages
Course materials
- Lecture slides can be downloaded from AMS Campus.
- Lab page
Additional teaching materials
The following papers are available only for teaching purposes.AI history
- Dartmouth summer research project proposal. The proposal for a project that has been the origin of AI.
- A.M. Turing, Computing machinery and intelligence (Mind, 1950)
Evolutionary computation
- Book (free download, Creative Commons) on genetic programming. R. Poli, W.B. Langdon, N.F. McPhee, J. Koza. A Field Guide to Genetic Programming.
Swarm intelligence
- M. Dorigo, E. Bonabeau, G. Theraulaz. Ant algorithms and stigmergy. Future Generation Computer Systems, Vol.16, n.9, 2000.
- C. Blum. Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2(4):353-373, 2005.
- H. Labella, M. Dorigo, J.-L. Deneubourg. Division of Labour in a Group of Robots Inspired by Ants' Foraging Behaviour. Technical Report IRIDIA-TR-2004-13, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium, 2005.
Artificial neural networks
- Hopfield networks on Scholarpedia. Where the concept of "Computers as dynamical systems" is discussed.
- Elman, J.L. (1990). Finding structure in time. Cognitive Science, 14, 179-211. PDF version from Elman's website. A prominent example of recurrent neural network.
Search
- Brief and informal introduction to computational complexity
- Search strategies exercises (in Italian). Credits Prof. Paola Mello.
CSP
- A brief introduction to mainstream techniques of constraint satisfaction. R.Barták. Constraint propagation and backtracking-based search. CP Summer school 2005.
- Incomplete depth-first search techniques: a short survey. R.Barták, Proceedings of CPDC 2004.
- Phase transitions and the search problem. T.Hogg et al., Artificial Intelligence, n.81, 1996.
Metaheuristics
- Introduction to metaheuristics. C.Blum, A.Roli. Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison. ACM Computing Surveys, Vol.35, N.3, 2003. (preprint available)
- A multi-agent architecture for metaheuristics. M.Milano, A.Roli. MAGMA: A Multiagent Architecture for Metaheuristics. IEEE Trans. on Systems, Man and Cybernetics - Part B, Vol.34, Issue 2, April 2004. (preprint available)
- A formal model of local search. A.Roli. A note on a model of local search. Technical report TR/IRIDIA/2004/23.01, IRIDIA, Université Libre de Bruxelles, Belgium.
- On the relation between complete and incomplete search. M.Milano, A.Roli. Proceedings of CPAIOR 2002.
- An introduction to Hybrid metaheuristics (slides).
- Large Neighbourhood Search Algorithms for the Founder Sequence Reconstruction Problem. A. Roli, S. Benedettini, T. Stuetzle and C. Blum. TR/IRIDIA/2010-012.
- An example of experimental analysis in R of examples of stochastic local search algorithms for the N-queens problem.
- Programming by Optimization. By Holger H. Hoos, in Communications of the ACM, Vol. 55 No. 2, Pages 70-80.1.1.1 Logics
Acknowledgements
I thank Prof. Paola Mello, Prof. Michela Milano and Prof. Giorgio Buttazzo for giving me the permission of using part of their teaching material, which was revised and emended for this course.AI*IA - Italian association for artificial intelligence
The Italian association for artificial intelligence is a non profit association promoting AI research. Website: www.aixia.it For further information, students can also contact Andrea Roli.
learning outcomes
The course aims at providing students the fundamentals of Artificial Intelligence (AI). Students will be able to tackle search problems using classical AI algorithms, such as uninformed tree-search algorithms, Best First Greedy and A*, as well as constraint satisfaction and constrained optimization problems by means of backtracking search, consistency techniques, constraint programming and metaheuristics. Moreover, students will know the basics of evolutionary computation, swarm intelligence techniques and neural networks. As far as knowledge representation and automatic reasoning is concerned, students will learn how propositional and first order logics are used in expert systems and classical planning techniques.
course contents
- Introduction
- Foundations of AI
- History of AI
- Application domains of AI
- Problem solving
- Solving Problems by Searching
- Non-informed and informed Search and Exploration
- Constraint Satisfaction Problems (complete and incomplete techniques: standard backtracking, constraint propagation techniques, local search)
- Adversarial Search: two players games, games with uncertainty
- Decision support systems and technologies
- Knowledge representation
- Inference in propositional and first order logic
- Reasoning
- Planning
- Introduction to planning and scheduling problems
- Planning and scheduling solving techniques (basics)
- Machine learning
- Reinforcement learning
- Evolutionary computation
- Neural networks
readings / bibliography
Russell, Norvig, "Artificial intelligence: A modern approach", Vol.1 and 2 (partially), second edition, Pearson/Prentice Hall
teaching methods
- Lectures in classroom
- Lectures in lab (AI software tools)
assessment methods
Written and oral examination.
teaching tools
- Lecture slides (in English) and web resources
- AI software tools
— a.y.
2011/2012
— credits
9
— cycle
1st cycle
— language
who
— professor
— other professors
— university
Alma Mater Studiorum-Università di Bologna
— campus
Cesena
— department / faculty / school
Ingegneria CE (0031)
URL & ID
— course ID
34781