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Course Name


Course Code

: 207008

Length of Course

: 17 weeks

Method of Dictation

: Technical - experimental

Hours per week

: Theory: 3h-Lab: 2 hours


: Vocational

Number of Credits

: Four (04)


: 205007 - Operations Research I


: 2012-I


: Hugo Vega
Teachers :
· Hugo Vega
· David Mauritius
· Rolando Maguiña


Artificial Intelligence, concepts, paradigms and applications in industry and services. Knowledge representation. AI problem representation as search in state space. Blind search methods and informed. Intelligent man-machine games. Expert systems, architecture, taxonomy and applications. Inference engine. Engineering knowledge, concepts, evolution, CommonKADS Methodology. Quality and Validation of Expert Systems, Introduction to Machine Learning (Machine Learning) and heuristic.


Students will gain knowledge in the area of ​​Artificial Intelligence in general and develop basic aspects of game development intelligent and expert systems, and its application in intelligent problem solving in the areas of industry and services.


After finishing the course, students will be able to:

1. Understand that is Artificial Intelligence and complexity of their problems.

2. Represent and solve human game - machine through search techniques in a state space.

3. Knowing the different search strategies blind and informed.

4. Design and develop game software intelligent man-machine interaction and artificial intelligence techniques used.

5. Understand what they are expert systems and know when to use.

6. Knowing which is the Knowledge Engineering and a method for developing knowledge-based systems

7. Assessing the quality of the expert system solution.

8. Design and develop expert systems based on different inference engines (methods chaining), considering quality criteria.

9. Understand the concepts of machine learning and heuristic, its importance and its applications in industry and services.

5. Course Content

Select the title of a topic to view it in presentation - Each week we will update-only until the 3rd

Temas Classes

Classification of algorithmic problems
  • 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

LispWork Tutorial

Foundations of Artificial Intelligence
  • 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

descargar Descargar Lisp

Search methods in a state space
  • 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

Problem of fox, hen and maize

Blind search methods
  • Blind search methods: breadth, depth and nondeterministic.

          References: [1] Chapter 3

Tutorial LispWork COMPLETO

Search methods reported
  • 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.

Examples of search trees with Lisp

Search methods for games Man - Machine
  • 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.

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Fundamentals of expert systems
  • 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

Tree binary Lisp
binary search tree breadth and depth Lisp


Partial exam answer key 2012-I
Partial exam answer key 2010-0

Presentation of computational work
  • Students show their skills in developing game software-based intelligent search techniques. You must present a report and software, and will exhibit their work.
family tree
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Knowledge Engineering
  • Introduction.
  • Acquisition of knowledge.
  • The methodology CommonKADS.
  • System Design Expert (SE).
  • Life cycle of a SE.

          References: [12]Capitulo 5 Subcapitulo 5.1

Knowledge Acquisition
  • 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

Development of rule-based expert systems
  • 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.

Quality and validation of expert systems
  • 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

Introduction to Machine Learning (Machine Learning) and heuristic
  • 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

Presentation of computational work
  • 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.



During the lab sessions will be developed in a basic programming language artificial intelligence is LIPS (or a variant thereof) or CLIPS and this is directed towards the development of rule-based expert systems. Also in the lab sessions can evaluate the progress of work.


The course is developed through theoretical - practical activities, emphasizing applications in industry and services. The students were divided into teams of 3 develop two computational work. During the theory sessions will discuss the proposed problem solving. During the lab sessions will assess the progress of the work computer and the process of learning a language of artificial intelligence.


Final Mean (PF) is determined as follows:

PF = 0.025 (CL1 + CL2 + CL3 + CL4) + 0.075 (TB1 + TB2) + 0.15 * 0.30 * LA + (EA + EB)

CLx: Reading Controls (CL1, CL2, CL3 and CL4)
TB1: Group Work (Games Smart Man - Machine)
TB2: Group Work (Expert Systems)
EA: Partial Review
EB: Final Exam
LA: Laboratory

The student may replace the exam partial or final if unable to provide any of these tests. Only the student will be evaluated for 70% or more of assistance.


[1]. STUART, RUSSELL, PETER, Norvig - 1996 Artificial Intelligence, a modern approach. Ed Prentice Hall.ISBN 0-13-103805-2

[2]. PATRICK, WINSTON - 1984 Artificial Intelligence. Addison-Wesley ISBN 0-201-51876-7

[3]. ELAINE RICH - 1988 Artificial Intelligence. Ed McGraw-Hill ISBN 0-07-450364-2

[4]. DAVID, Mauritius - 2000 Notes of Artificial Intelligence.

[5]. BONIFACE, MARTIN, ALFREDO, SANZ - 2002 Neural Networks and Fuzzy Systems. Ed ISBN Alfaomega 84-7897-466-0

[6]. Joseph Giarratano - GARY RILEY - 2001 Expert systems, principles and programming. Ed Thomson ISBN Sciences 970-686-059-2

[7]. PALMA JOSE M. MARIN ROQUE M. - 2008 Artificial Intelligence techniques, methods and applications. Ed Mc Graw Hill ISBN 978-84-484-5618-3

[8]. JOSE R. ROW, VICTOR J. MARTINE. - 2000 Artificial neural networks, foundations, models and applications. Ed Alfaomega - ISBN branch 978-84-484-5618

[9]. NILS J. NILSON - 2001 Artificial Intelligence, a new synthesis. Ed Mc Graw Hill ISBN 978-84-484-5618-3

[10]. PINE, GOMEZ, BELOW - 2001 Expert systems, artificial neural networks and evolutionary computation. Univ of Oviedo Ed ISBN 84-8317-249-6

[11]. Munarriz ALVAREZ, LUIS - 1994 Fundamentals of Artificial Intelligence Ed ISBN EDITUM 84-7684-563-4

[12]. FEDOR DE DIEGO, ALICE - 1995 Teria Terminology and Practice ISBN 980-237-096-7 Ed EQUINOX