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2016/2017 Challenge Results

Winner The winner of the 2016/2017 competition, with 9 wins and 5 draws out of 14 matches, is called Akatsuki. A sum up of the competition results can be found in the attached file "2017end". The detailed results are illustrated in the file "2017results", while the output of each match is available in the file "2017matches".   Partecipant Teams Any student who has partecipated is free to notify any inaccuracy to Andrea Galassi ( a.galassi *at* unibo.it ) Team 1: MoulinBleu   Team 2: Akatsuki  

2015/2016 Challenge Results

Winner

The winner of the 2015/2016 competition, with 14 wins out of 18, is called Samaritan.

 

Partecipant Teams

Any student who has partecipated is free to notify any inaccuracy to Andrea Galassi ( andrea.galassi7 *at* unibo.it )

Team 1: Alphabot

Binary representation of the board using two integers.

Iterative deepening search with alpha-beta pruning.

Multi-threaded idle-time searching.

The heuristic function is based on the value of the player's checkers.

Result achived: 10/18 wins.

Nine Men's Morris Challenge

Each year, as part of the course of Foundaments of Artificial Intelligence M, groups of students are invited to design a little prototype of an Artificial Intelligence able to play to the game of Nine Men's Morris, a popular board game also known as Mill, Cowboy Checkers, Merrils, Mulino, Mulinello Grisia, Tris, or Filetto.

 

2016/2017 Challenge

Slides of the current challenge (in italian): 2017 slides

 

Lectures, Lab & Seminars

Slides for the seminar will be downloadable from the dedicate page on the course web site.

Lectures

Lecure slides will be published as the course progresses:

Books & Papers

 Reference Books

About AI in general:

Program and Evaluation Process

Program:

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.

Intelligent Systems

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.

Modalità d'Esame

Modalità d'esame

L'esame per i due corsi integrati consiste di due prove:

  • Una prova pratica (programmazione in laboratorio), per Laboratorio di Informatica
  • Una prova scritta, per Analisi Numerica

Le due prove possono essere sostenute in qualunque ordine

  • Non è più necessaria (come precedentemente indicato) supeare la prova pratica prima dello scritto

I voti rimangono validi per un anno

Temi d'Esame

 Temi d'esame per allenamento

Alcuni temi d'esame di preparazione (sono più difficili della media e non sono mai stati effettivamente utilizzati per un appello):

DeepOpt - Encoding Deep Networks in Combinatorial Optimization Models

DeepOpt - Encoding Deep Networks in Combinatorial Optimization Models

Google Faculty Research Award 

Principal investigators: Michela Milano, Michele Lombardi

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