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ISMAT 2129

Artificial Intelligence

IT Engineering
  • ApresentaçãoPresentation
    It is intended that the student acquires an introductory knowledge about the fundamentals, techniques and practical applications of AI. To this end, the concept of agent is adopted. The development of agents of increasing complexity and capacity is studied, following three metaphors: symbolic, connectionist and biological. Due to the fundamental role they play in AI, the emphasis is placed on the concepts of state, operator of change of state and state space and on the modeling of problems through these concepts.
  • ProgramaProgramme
    S1. Introduction 1.1 AI definitions 1.2 Basics 1.3 The different paradigms 1.4 Brief historical perspective S2. Reactive Agents 2.1 General 2.2 Purely reactive agents 2.3 Reactive agents with memory S3. Search agents 3.1 Single agent issues 3.1.1 Blind search 3.1.2 Heuristic search 3.2 Problems with opposing agents 3.2.1 Minimax strategy 3.2.2 Alpha-beta and other cuts S4. Knowledge Based Agents 4.1 Deductive agents 4.2 Rule-based and Structure-based agents S5. Learning Agents: Connectionist Approach 5.1 General 5.2 Supervised learning 5.3 Unsupervised learning 5.4 Conclusions on the connectionist approach S6. Adaptive agents 6.1 Basic elements of genetic algorithms 6.2 Theoretical aspects: the scheme theorem 6.3 Advanced topics 6.4 Conclusions on the biological approach S7. Agents based on Large Language Model 7.1 Embeddings 7.2 RAG S8. Agent Companies 8.1 Two agents 8.2 Strategies, learning and adaptation 8.3 Multi-agents
  • ObjectivosObjectives
    LO1. Understand and know how to use the main tools available in the Artificial Intelligence area. LO2. Be able to develop new concepts and applications. LO3. Be able to apply the specific knowledge obtained to other areas of knowledge.
  • BibliografiaBibliography
    Russel, S. & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, 4th Edition. Prentice-Hall. Costa, E. & Simões, A. (2008). Inteligência Artificial. Fundamentos e Aplicações. (2.ª ed.). Lisboa: FCA. Negnevitsky, M. (2011). Artificial Intelligence: A guide to Intelligent Systems. (3rd ed.). Pearson Education.  
  • MetodologiaMethodology
    The content taught is implemented in software using programming. Active, problem-solving-oriented methodologies (PBL) are used. Applied projects allow you to deepen and integrate all the material taught.
  • LínguaLanguage
    Português
  • TipoType
    Semestral
  • ECTS
    6
  • NaturezaNature
    Mandatory
  • EstágioInternship
    Não