Teacher: Prof. Fabio Patrizi

Prerequisites. To successfully attend this course, students are required to be familiar, at a level acquired through academic courses, with the basics of Propositional and First-Order logic, Search Algorithms, Probabilities, Computational Complexity, and Object-Oriented Design & Programming. Some of these notions will be reviewed in the first part of the course. 

Objectives. The objective of the course is to introduce the basics of automated logical reasoning & planning as building blocks for designing and implementing modern autonomous agents. The course focuses on approaches for modeling discrete dynamic domains, automated reasoning, planning, and reasoning under uncertainty. Students will learn common approaches for automatically deriving new knowledge from a model and synthesizing strategies to achieve desired goals in dynamic domains.

Schedule. Lectures will be held in Via delle Sette Sale 29, starting Tuesday, Sep 30, 2025, according to the following schedule:

• • • Tuesday 16:00-18:00, Room 41

    • Friday 12:00-15:00, Room 41

Google Classroom (and Form).

We use Google Classroom. To obtain the registration code, you are required to fill out a Google form containing questions about your background in AI. It is required to sign in with your institutional account (@uniroma1.it). 

The form is available here.

Please fill out the form and register to Classroom ASAP!

Student hour. See here.

Exam Program (Preliminary)

Artificial Intelligence (6 CFUs):

• Introduction

• Search Algorithms (review): Uninformed and Informed Search Strategies. Heuristic Search. 

• Propositional Logic (review): syntax and semantics; evaluation, satisfiability, validity, logical implication; propositional tableaux; DPLL.

• First-Order Logic (review): syntax, semantics; evaluation, satisfiability, validity, logical implication; FO tableaux.

• Reasoning about Action: modeling discrete dynamic domains, action preconditions, effects, the frame problem, observability, determinism, stochasticity.

• Classical Planning: deterministic domains, PDDL (STRIPS, ADL), Heuristics for Planning

• Contingent Planning (FOND): nondeterministic domains, PDDL with oneof operator, nondeterministic planning by adversarial search, search in AND-OR Graphs

• Knowledge and Reasoning under Uncertainty: MDPs, Value Iteration, Policy Iteration

• Situation Calculus: precondition and successor-state axioms; situation tree; regression; executability and projection.

Teaching Material.

[1] Artificial Intelligence: A Modern Approach, Global Edition, 4th Edition by Stuart Russell, Peter Norvig, Pearson 2020 (selected chapters). 

[2] Slides and additional material (published during the course):