Introduction to Artificial Intelligence

Recommended Prerequisites

Programming courses, Mathematical Foundations.

Teaching Methods

Teaching methodologies:

- Seminar lectures with exposure of concepts (both theoretical and practical) materials and practice of concepts about the program

- Theoretical-practical classes with practice of CG concepts. These classes will be also used to introduce the individual practical works, its goals and fundamental ideas using the programming language "processing".

- Laboratory classes with practice of programming concepts in “Processing”

Adopted resources:

- Slides to support seminar lectures and knowledge synthesis. 

 

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 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

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
Mini Tests: 20.0%
Project: 20.0%
Exam: 60.0%

Bibliography

Daniel Shiffman, Learning Processing

Casey Reas, Ben Fry, Processing: a programming handbook for Visual Designers and Artists

Ira Greenberg, Processing: Creative coding and Computational Art

J. Foley, A. Van Dam, S. Feiner, J. Hughes, R. Philips, Introduction to Computer Graphics, Addison-Wesley.

D. Hearn, M. Baker, Computer Graphics, C Version, 2nd Edition, Prentice Hall

Apontamentos fornecidos pelo docente.