Learning objectives
Provide an introduction to modern Artificial Intelligence with a particular regard for the issues related to the various forms of logic reasoning.
Prerequisites
None
Course unit content
Artificial intelligence and agents
Problem solving via search
Games and adversarial problems
Constraint satisfaction problems
Logic-based agents
Planning
Structured knowledge representation
Learning
Neural networks
Multi-agent systems
Full programme
Artificial intelligence and agents
Chapters 1 and 2 of the textbook. An introduction to Artificial Intelligence and to the rational agent metaphor.
Problem solving via search
Chapters 3 and 4 of the textbook. Problem solving via search in the state space. Breadth and depth search. Informed search: the A* algorithm. Local search: genetic and evolutionary algorithms.
Games and adversarial problems
Chapter 5 of the textbook. Games via search in the state space: the minimax algorithm and alpha-beta pruning.
Constraint satisfaction problems
Chapter 6 of the textbook. Constraint satisfaction problems (CSPs). CSP solving via backtracking. Types of consistency and arc-consistency. Forward checking and algorithms for local consistency maintenance.
Logic-based agents
Chapters 7, 8 and 9 of the textbook. Propositional logic: clauses and resolution. First order logic and basics of resolution and logic programming.
Planning
Chapter 11 of the textbook. General characteristics of a planning system. The blocks world. STRIPS. Real-world planning: conditional planning and execution control.
Structured knowledge representation
Description logics and structured inheritance networks. Ontologies and their applications to the Semantic Web.
Learning
Chapter 18 of the textbook. Inductive learning: decision trees. Reinforcement learning.
Neural networks
Perceptrons and feed-forward networks. Reinforcement learning and the back propagation algorithm
Multi-agent systems
Cooperative and competitive agents and multi-agent systems. Communication between agents and communicative acts. FIPA and the BDI model (brief introduction to modal logics)
Bibliography
Stuart Russell e Peter Norvig. Intelligenza artificiale: un approccio moderno. UTET Libreria, 1998.
Material available at http://www.ce.unipr.it/people/bergenti/teaching
Teaching methods
Classes and laboratories are located at Dipartimento di Matematica.
Laboratory exercizes share time slots with classes.
Classes are allocated according to the calendar of Facoltà di Scienze MM.FF.NN.
Dates of exams will be available at http://informatica.unipr.it
Meetings with lecturers can be requested via e-mail.
Assessment methods and criteria
Oral exam and exercize project
Other information
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