Course Program

Artificial Intelligence (6CFU - De Giacomo)

  • Introduction to Artificial Intelligence

  • Knowledge Representation

    • Representing models of the domain of interest

    • Enabling autonomy

    • The role of logic

  • Propositional Logic

    • Propositional formulas, and knowledge-bases

    • Evaluation (Model Checking), Satisfiability, Validity, Logical Implication

    • Tableaux

    • DPLL, SAT solvers

  • First-Order Logic

    • Evaluation in First-Order Logic

    • Reasoning in First-Order Logic

    • Tableaux

    • UML Class Diagrams as Knowledge Bases

    • Incomplete information and Conjunctive Queries

  • Reasoning about Actions

    • Modeling dynamics of the domain of interest

    • Deliberating and executing actions

    • Action Preconditions, Effects, the Frame Problem,

    • Situation Calculus: Precondition Aximos, Successor State Axioms

    • Situation tree

    • Regression

    • Executability of sequences of actions and Projection (querying a situation resulting from action sequences execution)

  • Planning in Deterministic Domains

    • Deterministic Planning Domains

    • STRIPS, ADL, Planning Domain Description Language (PDDL)

    • Transition Systems

    • Planning by backward fixpoint computations

    • Planning by forward search, Heuristics, Best-first, A*

  • Planning in Nondeterministic Domains (FOND)

    • Nondeterministic Planning Domains

    • PDDL with oneof operator

    • Game Theoretic View

    • Nondeterministic Planning by backward fixpoint computations

    • Nondeterministic Planning by Adversarial Search, Search in AND-OR Graphs



Machine Learning (3CFU - Patrizi)

  • Introduction to Machine Learning

  • Evaluation

  • Supervised Learning:

    • Decision Trees

    • Bayesian Learning

    • Linear Classification

    • Linear Regression

    • Deep Learning

  • Unsupervised Learning

  • Reinforcement Learning