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Artificial Intelligence

Code:

INF32185

Sigla:

IA

Áreas Científicas
Classificação	Área Científica
OFICIAL	Informática

Ocorrência: 2021/2022 - 1S

Ativa?	Yes
Unidade Responsável:	Departamento de Sistemas e Informática
Curso/CE Responsável:	Informatics Engineering

Ciclos de Estudo/Cursos

Sigla	Nº de Estudantes	Plano de Estudos	Anos Curriculares	Créditos UCN	Créditos ECTS	Horas de Contacto	Horas Totais
INF	142	Plano de Estudos	3	-	6	75	162

Língua de trabalho

Portuguese

Objetivos

Give students knowledge of usage problems intelligence and artificial knowledge techniques, using knowledge methods. It is intended to endow students with the understanding and ability to develop search states in space and others used in game theory.

Resultados de aprendizagem e competências

students acquire
- technical capabilities of functional programming through teaching LISP
- abilities to work in groups and to present your work to an audience, through the presentation of projects
- ability to solve complex problems by teaching state space search

Modo de trabalho

Presencial

Pré-requisitos (conhecimentos prévios) e co-requisitos (conhecimentos simultâneos)

advanced programming knowledge

Programa

1. Introduction to Artificial Intelligence
1.1. Types of problems and solutions
1.2. Sub-areas of Artificial Intelligence
2. Deepening LISP as programming languages ​​for Artificial Intelligence
2.1. Recursion and functional programming
2.2. Atoms and Lists: data structures and functions; lambda functions.
2.3. The LISP evaluator; meta-functions
3. Troubleshooting
3.1. Problem definition and characteristics
3.1.1. combinatorial explosion
3.1.2. The role of knowledge
3.2. Search in State Space
3.2.1. exhaustive methods
3.2.2. Satisfaction of constraints
3.2.3. Informed methods; heuristics; algorithms
4. Knowledge engineering
4.1. Knowledge representation techniques
4.1.1. rule based systems
4.1.2. Representation of uncertain/incomplete knowledge
4.2. Inference Processes
4.2.1. Inference based on forward chaining and backward chaining
4.2.2. The RETE algorithm
4.3. Expert Systems Development Methodologies
5. Game theory
5.1. Games as state space search problems
5.2. The minimax algorithm
5.3. The alphabeta algorithm

Bibliografia Obrigatória

Elaine Rich;; Inteligencia Artificial
Stuart Russel and Peter Norvig; Artificial Intelligence: A Modern Approach
Robert Wilensky; Common Lispcraft

Métodos de ensino e atividades de aprendizagem

Theoretical classes: presentation with the aid of slides + formative and/or summative assessment.
Practical classes: learning the necessary concepts of the LISP programming language and solving programming problems in this language + formative and/or summative assessment.
Lab classes: Programming exercises to solve on the computer. Some accompaniment in the development of the projects of the discipline + formative and/or summative evaluation. In the laboratories, the pair-programming method will be used, including in the resolution of the series of exercises for evaluation.

Tipo de avaliação

Distributed evaluation with final exam

Componentes de Avaliação

Designation	Peso (%)
Teste	45,00
Trabalho de campo	45,00
Trabalho laboratorial	10,00
Total:	100,00

Componentes de Ocupação

Designation	Tempo (Horas)
Elaboração de projeto	45,00
Frequência das aulas	45,00
Trabalho escrito	10,00
Total:	100,00

Obtenção de frequência

The assessment includes two possible modes, one of them (B) aimed at students with the status of worker-student.

Mode A (continuous assessment):
Does not include exam.
This Mode can only be used in the normal season and not in the resource or special season.
The absence of any of the series of exercises to be carried out in theoretical (1), practical (1) and laboratory (4) classes results in zero values ​​being counted in that series. Note that the arithmetic mean of the 6 sets of exercises must have a grade equal to or greater than 7.0.
• Elements:
o 6 mini-tests (Exercises for evaluation)
o 2 programming projects (in LISP).
o Test, carried out in the last week of classes.
• Final Grade A = Exercises*10% + Test*45% + Project1*25% + Project2*20%
Obligatory to obtain a grade equal to or greater than 7.0 (on the scale 0-20) in each of the 4 assessment elements.
Mode B (assessment by exam):
The exercises of the classes will not be counted, nor will there be control of attendance. Does not include Test. Mode B can be used in any of the evaluation epochs.
• Elements:
o 2 programming projects (in LISP).
the final exam.
• Final Grade B = Project1*25% + Project2*20% + Final exam*55%
Obligatory to obtain a grade equal to or greater than 7.0 (on the 0-20 scale) in each of the 3 assessment elements

Concepts:
1) Mini-tests, ie series of assessment exercises to be carried out in theoretical, practical and laboratory classes, to address up to 30% of the UC's learning objectives, lasting between 15 and 60 minutes;
2) Test: Theoretical and practical assessment. Mandatory only for students who have opted for Mode A, to be carried out in the last week of classes, aiming to address between 30% and 50% of the UC's learning objectives, with a duration between 60 and 120 minutes;
3) Final Exam: Theoretical and practical assessment. Required only for students who have opted for Mode B
4) programming projects, mandatory for both evaluation modes. Projects will be carried out in groups of 2 people. There may be exceptional situations in which groups of 3 or individuals are accepted.
The documentation and LISP code referring to the projects will be subject to plagiarism analysis by suitable software tools, and from a certain degree of similarity (not very high) it will naturally lead to the cancellation of the project. Extreme plagiarism situations may give rise to disciplinary proceedings. All projects will be subject to individual oral assessment, which will include conceptual aspects and analysis of implementation in LISP.

Fórmula de cálculo da classificação final

Final Grade Calculation:
The grade corresponding to the final grade of the most favorable assessment method for each student will be called “Regular Grade”. The final grade will be obtained as follows:
• Final Grade = min (Regular Grade + Bonus; 20)
In which “Bonus” will be the value of an optional assessment component corresponding to the performance of extra exercises, this opportunity being offered to any student who wishes to do so, in any of the Modes and seasons.

It is considered that the student is approved at the time of assessment in which he manages to gather the scores in each of the assessment elements necessary to obtain a Final Grade greater than or equal to 9.5 (which rounds up to 10).