Download Artificial Intelligence

Artificial
Intelligence
Artificial Intelligence
Course Presentation
Summary
Artificial
Intelligence
Motivations
Course Plan
Resources
Exam Methods
Motivations
Artificial
Intelligence
Artificial Intelligence:
Machines that think and act like humans do
Voight-Kampff test in blade-runner
Motivations
Artificial
Intelligence
Artificial Intelligence:
Machines that solve complex problems
Google Self Driving car
Related areas
Artificial
Intelligence
AI highly interdisciplinary
Probability and Statistics
Robotics
Logics
Algorithms
Game Theory
Pattern Recognition and Machine Learning
Practical applications: Overview
Artificial
Intelligence
Surveillance
Environmental monitoring
Search and Rescue operations
Energy management
Service Robots
Games, entertainment and education
Computer Vision
Medical Diagnosis
Hardware/Software Verification
...
Service Robots/Entertainment: Cooperative
Foraging
Artificial
Intelligence
Decide who is in the best position to execute a task
Surveillance and Monitoring: mobile sensor
exploration
Artificial
Intelligence
A group of sensors cooperatively plans for most informative
paths
Surveillance and Monitoring: precisione
agriculture
Artificial
Intelligence
Analyse data from greenhouse sensor network to maximize
crop yield and minimize infection
(Post-doc: Alberto Castellini, Project: EXPO-AGRI)
Surveillance and Monitoring: Multi-Robot
Patrolling
Artificial
Intelligence
Allocate visit locations to a group of robots
Security: Active Malware Analysis
Artificial
Intelligence
Use Reinforcememnt Learning to analyse malware
behaviors (PhD: Riccardo Sartea)
Ride-Sharing: coalition formation
Artificial
Intelligence
Form groups of riders to minimize fuel consumption
(Post-Doc: Filippo Bistaffa)
Environmental Survey: Water Monitoring
Artificial
Intelligence
Intelligent drones to monitor water quality
Water Monitoring: High level control for the
drones
Artificial
Intelligence
Human interaction with team oriented plans
(PhD student: Masoume Raeissi)
Water Monitoring: Planning informative paths
Artificial
Intelligence
Active learning to devise informative paths for classification
(PhD student: Lorenzo Bottarelli)
Water Monitoring: perception for autonomous
behaviors
Artificial
Intelligence
Use computer vision to detect relevant features and
situations (Researcher: Domenico Bloisi)
Course Plan I
Artificial
Intelligence
Problem Solving: Search (about 4 Lessons)
Uninformed search (Breadth first, Depth First, Iterative
Deepening, etc.)
Informed Search (A*, Heuristics, Local Search and
Optimization)
Constraint Processing (CSP, COP) (about 4 lessons)
Contraint Satisfaction Problems, Constraint Network
and Graphical models
Basic techniques for CSP (Consistency enforcing,
Backtracking, Local Search)
Tree-Decomposition (Dynamic Programming)
Constraint Optimisation Problems
Course Plan II
Artificial
Intelligence
Multi-Agent Systems (about 2 lessons)
Distributed COPs
Reaching agreement
Prova parziale (approx. end of April)
Adversarial Search (1 lesson)
Plan representation and monitoring (1 lesson)
Logic and Agents (about 2 lessons)
Logical Agents
Background on Logic (propositional, FOL)
Inference (DPLL, Resolution)
Probabilistic Reasoning (about 5 lessons)
background on Probability
Bayesian Network
Inference (complete and approximate)
Markov Decision Processes and Reinforcement
Learning
Resources
Artificial
Intelligence
Text Books
Artificial Intelligence: a modern approach 2nd Editon
Russel and Norvig (English edition)
Constraint Processing R. Dechter
Other Material
Scientific Papers, Slides, etc.
Will be available on web site
Web Page link
http://profs.sci.univr.it/ farinelli/courses/ia/ia.html
Exam modalities
Artificial
Intelligence
Single-test mode
Single written test at the exam day
Partial test mode: Two tests C1 + [C2 or P]
C1 and C2: solve simple exercises/describe techniques
studied during the course
P:
project to be developed at home (see below)
only to the exams right at the end of the class (Summer
Session)
partial written test C1: half-way through the course C2:
at the end of the course.
project (P) can be done in collaboration with another
person
Final grade: 50%C + 50%[C1 or P]
Projects
Artificial
Intelligence
Project
Instructor will propose a set of projects
Students can: choose among the set of proposed
projects or propose other projects
Projects proposed by students must be validated by the
instructor
Projects usually involve a programming part (in the
language most appropriate for the project)
Students must hand to the instructor a report of the
project and developed code.
Have a look at past projects on the course web site