Ateneo - UPO

The course is logically composed by two modules: Artificial Intelligence (6 CFU) and Intelligent Decision Support Systems (3 CFU).

ARTIFICIAL INTELLIGENCE
State space search; Case-Based Reasoning; Uncertain knowledge
representation: probability theory and Bayesian Networks;
INTELLIGENT DECISION SUPPORT SYSTEMS
Decision Theory, Multi-attribute utility, Influence diagrams, One-shot and
sequential decisions.

S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach (4th
edition), Prentice-Hall.

Knowledge and understanding: the class will introduce the fundamentals notions and the goals of intelligent agents by considering the current vision of modern AI.
Applying knowledge and understanding:
the acquired knowledge will allow the student:
to understand algorithms to perform state-space search.
to understand the architecture and the inference methods for solving problems in precedent-based systems (case-based reasoning).
to understand the role of uncertainty in a knowledge-based system.
to understand and apply modeling and analysis techniques concerning formalisms based on probabilistic networks.
to understand and aplly main notions about decision theory in presence of uncertainty and multi-attribute utility;
to extend notions of probabilistic networks to decision networks, in order to model and analyze one-shot and sequential decisions.
to use and apply suitable tools for decision problem analysis.

Making judgements: contents in this class will allow the student to develop autonomous judgement skills; he/she will be able to evaluate different choices from both representational and inference point of view, in a way to design a suitable intelligent decision support system.

Communications skills: the student will develop relational and communication skills that will allow him/her to work in team, and to emphasize pros and cons of the possible approaches.

Learning skills: the study of the principles of the discipline will allow the student to have a correct understanding of the possible alternatives in the development of an intelligent decision support system, and it will allow him/her to choose the most suitable approach.

Suggested: Algorithms and data structures Probability.

Lectures and exercises using Moodle and software tools.

Video lectures available

Written and oral (not mandatory) exam. Potential project presentation.

ARTIFICIAL INTELLIGENCE:
Introduction and history of AI State space search: path-based blind and
heuristic search. A* algorithm. Iterative improvement algorithms: hillclimbing
and simulated annealing CSP problems Two players games:
minimax and alpha/beta pruning Knowledge representations: hints on
logical formalism. Case-Based Reasoning systems; the CBR-Works tool.
Uncertain knowledge: probability theory, Bayesian Networks (properties
and algorithms); BN tools (Genie, Hugin, etc...).

INTELLIGENT DECISION SUPPORT SYSTEMS:
Decision Theory.
Multi-attribute utility Influence diagrams One-sot and
sequential decisions.

KNOWLEDGE: to know the main features and the architecture of an intelligent knowledge-based system.
To know main AI state space search algorithms.
To be able to represent knowledge in intelligent systems even when uncertainty is present.
To know the main inference algorithms adopted in modern intelligent systems.

To be able to model ad analyze one-shot and sequential decision processes, using probabilistic graphical models.
COMPETENCE AND SKILLS: to be able to
understand the role of different architectural modules of an intelligent agent.
To design agent or modules of agents able to solve problems in a state space, to solve problems based on precedents, to solve problems with uncertain knowledge, to suggest decision under uncertainty.

To be able to connect notions present in different parts of the class; to be able to suitably interface with domain experts; to know the right terminology.