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Facultade de Informática
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Máster Universitario en Intelixencia Artificial
 Asignaturas
  Razoamento e Planificación
   Contidos
Temas Subtemas
Unit 1. Introduction - knowledge representation (KR), reasoning about actions
- example-based methodology, declarative problem solving
- KR goals, elaboration tolerance, STRIPS language
- frame problem and inertia, non-monotonic reasoning, KR topics
Unit 2. Propositional Reasoning. - propositional logic, syntax and semantics, set of models
- entailment, inconsistence, tautology, deduction theorem, weaker/stronger formulas
- deduction/abduction/induction, from language to formulas, the SAT problem
- computational complexity, NP-completeness
- SAT solvers, Conjunctive Normal Form (CNF)
Unit 3. Rule-based Reasoning - Closed World Assumption (CWA), positive programs, least model, TP immediate consequences
- default negation, program reduct, stable models
- examples getting stable models, stratified programs
- choice rules, constraints, splitting
- Here-and-There (HT)
- Equilibrium models, strong equivalence
Unit 4. Relational Reasoning - grounding, deductive databases, Datalog, domain independence, safety
- Hamiltonian cycles, Answer Set Programming (ASP), GDT methodology
- Pooling, terms, reification, aggregates
- Optimisation
- ASP applications and solvers
Unit 5. Temporal Reasoning and Planning - telingo, switches example, simulation, postdiction, planning
- missionaries and cannibals, the blocks world
- abduction, explanation, diagnosis
- temporal equilibrium logic
- survey on AI planning

Unit 6. Terminological Reasoning 		Description Logics
Unit 7. Reasoning with inaccurate information - Categorical models
- Probabilistic models
- Cuasi-probabilistic models
- Certainty factors
- Theory of Evidence
- Fuzzy Logic
- Vectorial Approaches
- Quantum Models
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