This web site is dedicated to the Intelligent Systems M course. The web site aims at providing the students with access to information related to the goals, contents, texts and assessment criteria of the course.
Course schedule: see the information system of the School of Engineering.
Prof. ssa Michela Milano, Tel. 051 20 93790, michela <dot> milano <at> unibo <dot> it
Office hourse: Thursday from 10 to 12 (ex-CSITE building, above lecture room 8.1)
Dott. Michele Lombardi, Tel. 051 20 93270, michele <dot> lombardi2 <at> unibo <dot> it
Office hours: Tuesday from 10 to 12, on appointment to be booked by email ("Aule Nuove" building, close to lecture room 5.7)
The course takes advantage of some of the topics previously discussed in the Artificial Intelligence course (year 1), such as knowledge representation, logic, informed search strategies, game theory, constraint resolution. The Intelligent System course starts from such bases and aims at presenting the main applications of Artificial Intelligent methods, with practical examples.
The main goals of the course are:
- Apply the techniques learned in the Artificial Intelligent course (year 1) to complex problems
- Investigate complex problems and the main formal and algorithmic tools to address them
- Provide practical examples
- Learn how to peforme a critical readout of a scientific paper concerning the topics of the course
- Provive a practical approach to solve real problems
- Learn how to prepare a presentation, similar to that needed for the Master Thesis
- Give a chanes to the students to attend seminars from researchers actively involved in advanced AI topics
Some interesting links
Program and Evaluation Process
The course features both regular lectures and lab sessions. For each of the considered topics, a lab session will be scheduled after the lectures, so as to provide a chance to learn the software systems related to the presented topic. The techniques discussed in this course represent the state of the art of scientific research in Artificial Intelligence: for each of the consdidered topic, the students will be referred to survey paper that provide a good overview of state of the art research.
The detailed program is as follows:
- Non linear planning
- Hierarchical planning
- Graph-based planning
- Swarm Intelligence
- Ant-colony Optimization
- Particle Swarm Optimization
- Constraint Programming and Optimization
- Advanced Search and Propagation Strategies
- Machine Learning
- Decision Trees
- Neural Networks
The final exams consts of a written test containing both exercises and questions about theoretical topics. For this reason, it will not be possible to bring notes and books to the test.
Optional Course Project
It is possible to opt (by formally requesting it when planning the courses to take) for a course project in Intelligent Systems. In such a case, the project topic should be defined together with the course teacher.
The project may involve using an existing system to solve a complex problem , or the development of a new tool to solve for an AI application. The student should provide:
- An accurate report about the project contents and the developed code
- A presentaion (i.e. slides) to summarize the main steps of the projec, which will serve as a basis to guide the discussion
- The project code
Books & Papers
About AI in general:
- S. J. Russel, P. Norvig: "Artificial Intelligence: A Modern Approach", Prentice Hall International, Pearson Education Italia, 2005.
- E. Rich, K. Knight: "Intelligenza Artificiale", McGraw Hill, Seconda Edizione 1992.
- E. Charniak, D. McDermott, "Introduzione all'Intelligenza Artificiale", Masson, 1988.
- M.Ginsberg: "Essentials of Artificial Intelligence", Morgan Kaufman, 1993.
- P. H. Winston: "Artificial Intelligence: Third Edition", Addison-Wesley, 1992.
- L.Console, E.Lamma, P.Mello, M.Milano: "Programmazione Logica e Prolog", Seconda Edizione UTET, 1997 [web page for the book (in Italian)]
- I. Bratko: "Programmare in Prolog per l'Intelligenza Artificiale", Masson e Addison-Weslay, 1988.
- Ian Witten, Eibe Frank: "Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations", ISBN: 1-55860-552-5, Morgan Kaufmann Publishers, 2000.
Articoli scietifici relativi ai contenuti del corso verranno pubblicati con l'avanzare del programma.
- Life-long planning A* [paper]
- Partial Order Planners:
- Graph based planning:
- Contingency Planning:
- A paper by Hopfield about Neural Networks [paper]
- Originale paper about back-propagation [paper]
- A survey by Jean-Charles Regin about global constraints [paper]
- A book chapter by the same author (this one is longer and more detailed) [paper]
- A tutorial about Constraint Based Scheduling [paper]
Lectures & Lab
Slides for the seminar will be downloadable from the dedicate page on the course web site.
Lecure slides will be published as the course progresses:
Slides and data files will be published as the course progresses
- Automated planning
- Slides [pdf]
- Cart example (graphplan) [zip]
- Cart example (PDDL) [zip]
- An intrdoduction to PDDL [pdf]
- Quick PDDL reference guide [html]
- Planner executables (for linux, oldish compiler...) [zip]
- Install gcc-multilib and g++-multilib in Ubuntu to make them work
- Multi-agent simulation with NetLogo
- Decision Trees
- Slides [pdf]
- C4.5 Test [zip]
- Weka Test [zip]
- Language recognition problem [zip]
- Shape recognition problem [zip]
- Python cheat-sheet (basic concepts) [pdf]
- Python cheat-sheet (methods and functions) [pdf]