## Disciplina: Sistemas Inteligentes / Inteligência Artificial

### Important Note

All examinations, programming assignments, and written homeworks must be done by each team. Cheating and plagiarism will be dealt with in accordance with University procedures (see below the two documents related to the topic). Hence, code and reports for programming assignments should not be shared across teams. You are encouraged to discuss ideas, approaches and techniques with your peers, but code and reports should be unique for each team. Additionally, text written in reports should not be copied from internet sources. The same holds true for code. Please, have a look at the documents below.

### Theoretical Classes

• Class 1 (Feb 06): Introduction
• Class 2 (Feb 09): Procura não informada (revisão de busca em largura e profundidade)
• Class 4 (Feb 16): Procura informada (guloso com heurística, A*, IDA*, RBFS)
• Class 5 (Feb 20): Procura informada (Exemplo de execução do algoritmo A*) (cont.) e verificação de solvabilidade de problemas N*N-1
• Class 6 (Feb 23): Procura informada (guloso com heurística, IDA*, RBFS) (cont.)
• Class 7 (Feb 27): Algoritmos de melhoramento iterativo
• Mar 02: Dias Abertos (Open Day UP): no class
• Class 8 (Mar 06): Algoritmos de melhoramento iterativo: Simulated Annealing e Algoritmos Genéticos
• Class 9 (Mar 09): Adversarial Search: minimax and alpha-beta pruning
• Class 10 (Mar 13): Adversarial Search: minimax and alpha-beta pruning (cont.)
• Presentation by Tadeu Freitas and Nuno Silva: xadrez robótico (video1, video2)
• Presentation by Jhonny Moreira: Monte Carlo Tree Search (slides)
• Class 11 (Mar 16): Adversarial Search: minimax and alpha-beta pruning (cont.)
• Class 12 (Mar 20): FIRST TEST
• Class 13 (Mar 23): Constraint Satisfaction Problems
• Class 14 (Mar 27): Introduction to Prolog (language basics and theory)
• Class 15 (Mar 30): Introduction to Prolog (DCGs and other example)
• Class 16 (Apr 3): Introduction to Planning
• Class 17 (Apr 6): Planning: POP and CPOP
• Apr 10, 13, 17: Happy Easter!
• Class 18 (Apr 20): Planning: CPOP (threat resolution)
• Class 19 (Apr 24): Machine Learning: Decision Trees
• Class 20 (Apr 27): Machine Learning: Decision Trees (cont.)
• May 1st: Labour Day
• Class 21 (May 4): Probabilistic Models
• Class 22 (May 15): Probabilistic Models (cont.)
• Class 23 (May 18): Neural Networks
• Class 24 (May 22): Neural Networks (cont.) and Review
• Class 25 (May 24): SECOND TEST

• ### Practical Classes

• Week 1 (Feb 06-10): resolução de problemas e estratégias mentais (jogo dos oito, n-rainhas, jogo do galo e extração de padrões)
• Week 2 (Feb 13-17): Trabalho 1: Procura inteligente (pdf, html)
• Week 3 (Feb 20-24): Trabalho 1: Procura inteligente (cont.) (pdf, html)
• Week 4 (Feb 27-Mar 03): Trabalho 1: Procura inteligente (cont.) (pdf, html)
• Week 5 (Mar 06-10): Trabalho 2: Adversarial Search (pdf, html)
• Week 6 (Mar 13-17): Trabalho 2: Adversarial Search (cont.) (pdf, html)
• Week 7 (Mar 20-24): Trabalho 2: Adversarial Search (cont.) (pdf, html)
• Week 8 (Mar 27-31): Trabalho 3: Using Prolog (pdf, html)
• Week 9 (Apr 3-7): Trabalho 3: Using Prolog (cont.) (pdf)
• Apr 10-14: EASTER WEEK
• Week 10 (Apr 17-21): Trabalho 3: Using Prolog (cont.) (pdf)
• Week 11 (Apr 24-28): Trabalho 4: Decision Trees (pdf, html)
• Week 12 (May 1-5): Trabalho 4: Decision Trees (cont.) (pdf, html)
• Week 13 (May 15-19): Trabalho 4: Decision Trees (cont.) (pdf, html)

• What is the Turing Test? (seminal article by Alan Turing)
• 3,000,000 queens in less than one minute (Artigo sobre soluções eficientes para o problema das n-rainhas - pdf )

• MIT News - AI
• Science Daily - IA
• AI News
• The Future of AI (essay)
• Artificial Intelligence: a Modern Approach
• Weka
• RapidMiner
• Yap Prolog
• Aleph
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