Artificial Intelligence Fundamentals

Recommended Prerequisites

Programming courses, Mathematical Foundations

Teaching Methods

The unit includes theoretical lectures where the fundamental concepts, principles and techniques are presented and explained in detail. Their application to real world situations is also explored.

 

Lectures of theoretical-practical nature play the role of strengthening the connection between theoretic knowledge and their practical application.

 

The laboratory classes focus on: (i) development of the projects (ii) solving exercises.

 

The evaluation includes an exam, which is worth of 60% of the final grade, and a practical component, worth 40%. 

Learning Outcomes

The goals are acquisition of solid base knowledge on the field of artificial intelligence in terms of foundations, techniques and practical application. To serve this purpose the development of agents of increasing complexity and capabilities inspired in three different metaphors – symbolic, connectionist and biological – is studied. Considering the key role they play, particular relevance is given to the concepts of state, state change operator, and state space.

 

The main competencies to be developed are:

Instrumental – analysis and synthesis, problem solving

Personal – critical thinking

Systemic - practical application of the theoretical knowledge; research

 

The secondary competences are:

Instrumental – organizing and planning

Personal – work in teams

Systemic – autonomous learning; creativity

Work Placement(s)

No

Syllabus

1. Introduction
The. Defining Artificial Intelligence
B. Agents
ç. Rooms
d. Tasks
and. State, state change operator, state space
2. Fixed structure agents
The. Reactives
B. Demand
3. Agents with variable structure
The. Apprentices
B. Adaptive
4. Agent Society
5. Representation, Knowledge, Uncertainty, Reasoning

The following topics are covered for each type of agent:
i. Architecture
ii. Representation and reasoning
iii. Implementation according to metaphor: symbolic, connectionist, biological
iv. Application to problems

Head Lecturer(s)

Fernando Jorge Penousal Martins Machado

Assessment Methods

Assessment
Project: 40.0%
Exam: 60.0%

Bibliography

Costa, E., Simões, A.: Inteligência Artificial - Fundamentos e Aplicações, FCA - Editora de Informática, 2008.

 

Russell, S., Norvig, P.: Artificial Intelligence - A Modern Approach. Pearson Education 2010: I-XVIII, 1-1132