Learning objectives
Provide an introduction to modern Artificial Intelligence with a particular regard for the issues related to the various forms of logic reasoning. <br />
Prerequisites
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Course unit content
<p><strong>Artificial intelligence and agents <br />
</strong>Chapters 1 and 2 of the textbook. An introduction to Artificial Intelligence and to the rational agent metaphor. <br />
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<strong>Problem solving via search <br />
</strong>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. <br />
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<strong>Games and adversarial problems <br />
</strong>Chapter 5 of the textbook. Games via search in the state space: the minimax algorithm and alpha-beta pruning. <br />
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<strong>Constraint satisfaction problems <br />
</strong>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. <br />
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<strong>Logic-based agents <br />
</strong>Chapters 7, 8 and 9 of the textbook. Propositional logic: clauses and resolution. First order logic and basics of resolution and logic programming. <br />
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<strong>Planning <br />
</strong>Chapter 11 of the textbook. General characteristics of a planning system. The blocks world. STRIPS. Real-world planning: conditional planning and execution control. <br />
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<strong>Structured knowledge representation <br />
</strong>Description logics and structured inheritance networks. Ontologies and their applications to the Semantic Web. <br />
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<strong>Learning <br />
</strong>Chapter 18 of the textbook. Inductive learning: decision trees. Reinforcement learning. <br />
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<strong>Neural networks <br />
</strong>Perceptrons and feed-forward networks. Reinforcement learning and the back propagation algorithm <br />
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<strong>Multi-agent systems <br />
</strong>Cooperative and competitive agents and multi-agent systems. Communication between agents and communicative acts. FIPA and the BDI model (brief introduction to modal logics) <br />
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Full programme
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Bibliography
Stuart Russell e Peter Norvig. Intelligenza artificiale: un approccio moderno. UTET Libreria, 1998. <br />
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Material available at http://www.ce.unipr.it/people/bergenti/teaching <br />
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Teaching methods
Classes and laboratories are located at Dipartimento di Matematica. <br />
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Laboratory exercizes share time slots with classes. <br />
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Classes are allocated according to the calendar of Facoltà di Scienze MM.FF.NN. <br />
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Dates of exams will be available at http://informatica.unipr.it <br />
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Meetings with lecturers can be requested via e-mail.
Assessment methods and criteria
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Other information
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