Introduction to Artificial Intelligence

Recommended Prerequisites

Programming Skills: Introduction to Programming and Problem Solving, Principles of Procedural Programming.

Mathematical Skills: Discrete Structures, Statistics, Theory of Information.

Teaching Methods

The unit includes theoretical lectures where the fundamental concepts, principles, techniques and there applications to real world situations are presented and explained in detail.

Theoretical-practical lectures strengthen the connection between theoretic knowledge and its practical application. We focus on problem solving and on the analysis of case studies that require combining different theoretical concepts and that promote critical reasoning.

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

Learning Outcomes

This curricular unit aims at the acquisition of solid base knowledge on the field of artificial intelligence in terms of: foundations, techniques and practical application. To serve this purpose the integrating concept of Agent is adopted. 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
   1. Defining Artificial Intelligence
   2. Agents
   3. Environments
   4. Tasks
   5. State, state change operator, and state space
2. Agents with fixed structure
   1. Reactive
   2. Search
3. Agents with variable structure
   1. Larning
   2. Adaptive
4. Society of Agents
5. Representation, Knowledge, Uncertainty, Reasoning

 

The following topics will be addressed for each type of agent:

1. Arquitecture
2. Representation and Reasoning
3. Implementation according to each of the following metaphors: symbolic, connectionism, biologica
4. Aplication to problems

Head Lecturer(s)

Fernando Jorge Penousal Martins Machado

Assessment Methods

Assessment
A practical component: 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