Topics on Artificial Intelligence

Recommended Prerequisites

Programming and mathematical competencies

Teaching Methods

Theoretical classes will comprise detailed presentation of Artificial Intelligence concepts, principles and fundamental theories.
Practical Lab classes adopt a Project Based Learning approach, directed towards competence acquisition through the development of a laboratory work, comprising three components: (i) analysis of Artificial Intelligence works described in the literature, (ii) implementation, and (ii) writing of a scientific article, describing the lab work done, plus its presentation and defense.

Learning Outcomes

The main goal of this Curricular Unit is to introduce fundamentals of Artificial Intelligence, in all different components, from knowledge representation to reasoning and decision-making, and to learning, in order to support advanced Curricular Units of applicational or research nature.
With this Curricular Unit the students are supposed to acquire knowledge on the fundamentals of Artificial Intelligence and develop skills in analysis and synthesis, critical thinking, problem solving, independent learning, ability to plan and decide.

Work Placement(s)

No

Syllabus

1. Introduction
a. Definition
b. Agents, Tasks and Environments
c. States, State change operators, State Space
2. Fixed-Structure Agents
a. Reactive
b. Search
- Blind
- Informed
- Stochastic
- Adversarial
c. Knowledge Based
- Knowledge Representation and Reasoning
- Semantic Networks, Ontologies
- Logic
- Inference and Reasoning
- Semantic Web / Linked Data
- Uncertainty
3. Variable-Structure Agents
a. Learning
- Supervised
- Semi-supervised
- Non-supervised
b. Adaptive
4. Perception and Action
b. Vision
c. Sound
e. Natural Language Processing
d. Robotics
5. Autonomous Agents and Multi-Agent Systems
a. BDI Architecture
b. Agent-oriented software engineering
c. Nature inspired approaches
d. Agent communication
e. Negotiation and argumentation
f. Cooperation and coordination
g. Agent-based Modelling and Simulation
6. Planning

Head Lecturer(s)

Luís Miguel Machado Lopes Macedo

Assessment Methods

Assessment
Exam: 50.0%
Project: 50.0%

Bibliography

- Russell, Stuart, and Norvig, Peter. Artificial Intelligence: a Modern Approach, 3rd. Edition, Prentice Hall, 2010.
- Wooldridge, Michael. An introduction to MultiAgent Systems, 2nd. Edition, John Wiley, 2009.
- Shoham, Y. & Leyton-Brown, K .2009.  Multiagent Systems – Algorithmic game-theoretic and logical foundations. Cambridge University Press.
- Costa, E. and Simões, A., Inteligência Artificial: fundamentos e aplicações (3ª edição), FCA, 2008.