• Presentation of the course.
• Classification of algorithmic problems, problems P and NP.
• Decision problems, localizaciny optimization. Description of some NP-hard problems.

          References: [1] Chapter 3

• Definition of Artificial Intelligence. Intelligent machine.
• Difference between operating systems and intelligent systems.
• Review of artificial intelligence languages.
• Applications in industry and services. (Robotics, planning, waste management)
• Turing Test

          References: [1] Chapter 1, [2] Chapter 1,
          [10] Chapter 1, [10] Chapter 2

• Definition of AI problems as search problems in a state space.
• Representation of problem gamblers human - machine.

          References: [1] Chapter 3, [3] Chapter 2,
          [4] Chapter 3, [11] Chapter 2 Sub 3B,
          [11] Chapter 2 Sub 3C

• Blind search methods: breadth, depth and nondeterministic.

          References: [1] Chapter 3

• Methods that use additional information: first the best, climb the hill, branch and bound.
  

          References: [1] Chapter 4, [2] Chapter 5,
          [3] Chapter 3, [4] Chapters 5 and 6.

• MIN-MAX method for developing intelligent human-machine games.
  
  

          References: [1] Chapter 6, [2] Chapters 5
          and 6, [3] Chapters 3 and 12,
          [4] Chapters 5 and 6.

• Definition of Expert Systems.
• Architecture of an expert system.
• Taxonomy and expert systems applications. Requirements for the development of expert systems and advantages of the use of expert systems.
• Some problems based on knowledge.

          References: [10] Chapter 2A [10] Chapter 2B
          [11] Chapter 3,[6] Chapter 1

• Students show their skills in developing game software-based intelligent search techniques. You must present a report and software, and will exhibit their work.

• Introduction.
• Acquisition of knowledge.
• The methodology CommonKADS.
• System Design Expert (SE).
• Life cycle of a SE.

          References: [12]Capitulo 5 Subcapitulo 5.1

• Acquisition of knowledge.
• Construction of the basis of facts and knowledge base.
• Knowledge representation structures (rules of inference, frames, objects, semantic networks, predicate logic).

      References: [10] Chapter 4 Subchapter C

• Construction of the basis of facts and knowledge base.
• The inference engine.
• Backward chaining methods, progressive and reversible. Matching techniques, the RETE algorithm.
• Conflict resolution techniques.

          References: [3]Chapter 3, [3]Chapter 1_A,[3]Chapter 1_B,
          [2] Chapter 7, [6] Chapter 3, [7] Chapter 3.

• Major errors in the development of an expert system. Quality of an expert system. Validation of intelligent systems, quantitative methods validation.
• Efficiency expert systems and error. Review of the functionality of SE 2nd Job.
• Tasks: quality and validation exercises SE, validate the proposed system work 2nd.

          References: [7] Chapter 21

• Concepts of learning and machine learning.
• Expert systems, machine learning vs..
• Learning techniques and stages of development of machine learning.
• Machine learning applications in industry and services.
• Concepts of heuristics and meta-heuristics. Heuristic algorithms vs. exact algorithms.
• Heuristics and meta-heuristics. Combinatorial optimization problems in industry and services.

          References: [5] Chapters 1 and 2

• Students show their skills in the development of expert systems and their applications in the industry and service. Students will present a report and software.