Course Program

Artificial Intelligence (6CFU)

• 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 Axioms, 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

• Introduction to Machine Learning

• Basics on Probability (Review)

• Supervised Learning:

  • Linear Classification

  • Linear Regression

  • Neural Networks 

• Unsupervised Learning

• Reinforcement Learning