En construccion

Artificial intelligence

Hugo Vega Huerta 2014 - II

NATIONAL UNIVERSITY OF SAN MARCOS

(University of Peru, DEAN OF AMERICA)

FACULTY OF ENGINEERING AND COMPUTER SYSTEMS

Academic Professional School of Systems Engineering

Syllabus

 

1. GENERAL SPECIFICATIONS

Course title	: Artificial intelligence
Course Code	: 207008
Course Length	: 17 weeks
Form Dictation	: Technical - experimental
Weekly hours	Theory: 3h - Laboratory: 2h
Nature	: Vocational
Number of Credits	: Four (04)
Pre-requisites	: 205007-Operations Research I
Semester	: 2014-2

2. INTRODUCTION

Artificial intelligence is an area of computer science that aims to develop practical and theoretical foundations for the development of computer systems that exhibit intelligent features and usually correspond to intractable problems.Many problems occurring in industry, services and entertainment are smart problems and their solution becomes increasingly essential in organizations due to the demands of competitiveness which are increasing every day.

Among the various types of system are intelligent human-machine games, knowledge-based systems, and the optimization system learninng machine.

In this course, an introduction to artificial intelligence and its applications in industry, services and entertainment is, and how the artificial intelligence based technologies can create value and make organizations more competitive.Applications will also be displayed in software engineering.

It discusses further the design and implementation of human-machine gambling and knowledge-based systems.

3. ACHIEVEMENT OF COURSE

After completing the course the student will acquire general knowledge of the area of artificial intelligence, design and implement human-machine games competition based on artificial intelligence and knowledge-based systems, using clear and accurate search techniques in a space status and CommonKADS methodology.

4. UNITS OF LEARNING

5. CONTENTS FOR WEEKS:

WEEK	CONTENT	CLASSES	JOBS
1	Foundations of Artificial Intelligence Definition of Artificial Intelligence. Intelligent machine.difference between systems (robotics, planning, waste management). Turing Test References: [1] Chapter 1, [2] Chapter 1, [9] Chapter 1.	Class Week1
2nd	Classification of algorithmic problems Presentation of the course. Classification of algorithmic problems, problems P and NP. Decision problems, localization and optimization. Description of some NP-hard problems. References: [4] Chapter 1 [1] Annex A.	Class Week 2	TASK EXPERT SYSTEMS Exercises in LISP
3rd and 4th	Representation of human gambling problems - search machine as a state space  Definition of AI problems as search problems in a state space. Representation of problems of human game - machine.  References: [1] Chapters 3, [3] Chapter 2, [4] Chapter 3.	Class Week 3 Class week 4	Family tree     Trees Exercise Functions   Exercises Lisp functions
5th	Methods of blind search  The search problem in a state space as graph. The inability to use methods shortest path. The concept of the methods of blind and informed search. The state tree. The blind methods: breadth, depth, non-deterministic.  References: [1] Chapter 4, [2] Chapter 5, [3] Chapter 3, [4] Chapters 5, [9] Chapters 9.	Week 5	Problem Gallina Zorro Maiz Travel and binary-tree search Width and depth LISP
6th	Search methods reported The evaluation function, methods that use additional information : first the best, climb the hill, A *, branch and bound.  References: [1] Chapter 4, [2] Chapter 5, [3] Chapter 3, [4] Chapters 5, [9] Chapters 9.	Class Week 6	Avara Search - Cities in Mexico
7th	Search methods for human game - machine  Algorithm human game - machine. Strategies machine game: no deterministic best-first, min-max and better profit difference. Min-max algorithm and alpha-beta.  References: [1] Chapter 5, [2] Chapters 6, [3] Chapters 4, [4] Chapters 6, [9] Chapters 12.	ClaseSemana 7-1 Class Week 7-2	Michi Minimax and Roads With Heuristica
8th	Partial Examination		Midterm Exam
9th	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 using expert systems. Some problems based on knowledge.  References: [6] Chapter 1.
10 °	Knowledge Engineering  Introduction. Acquisition of knowledge. C ommonKADS methodology. Design of Expert Systems (ES). Lifecycle of an SE.  References: [6] Chapters 6, [7] Chapter 19.
11 °	Knowledge Acquisition  Acquisition of knowledge. Construction of the basis of facts and knowledge base. Knowledge representation structures (rules of inference, frames, objects, ontologies, metadata, thesaurus).  References: [6] Chapters 6, [7] Chapter 19.		Sustainability Neural Networks Solution
12 °	Developing rules based expert systems  Construction of the basis of facts and knowledge base.The inference engine. The methods of regressive, progressive and reversible chain. Matching techniques, the RETE algorithm. Conflict resolution techniques.  References: [1] Chapters 6 and 8, [2] Chapter 7 [6] Chapter 3, [7] Chapter 3.		Expert system - Dog World Correction transport expert system
13 °	Quality and validation of expert systems  Major errors in the development of an expert system.Quality of an expert system. Validation of Intelligent Systems, cuantit ative methods validation. Efficiency and error of expert systems. Reviewing the functionality of SE 2nd job.  Tasks: quality and validation exercises SE, validate the proposed system of 2nd job.  References: [4], [7] Chapter 21.		Qualified Practice SE SE counters, recursion and 3 or more input variables
14 °	Introduction to Machine Learning and heuristics  Concepts of learning and machine learning. Vs machine learning expert systems. Learning techniques and stages of development of machine learning. Machine learning applications in industry and services. The combinatorial optimization problem. Concepts of heuristics and meta-heuristics. Exact Algorithms vs heuristic algorithms. Heuristics and meta-heuristics.Combinatorial optimization problems in industry and services.  References: [10], [11], [5] Chapter 1, [8] Chapter 1.
15 °	Presentation of computational work  Students show their skills in the development of expert systems and their applications in the fields of industry and service. Students will present a report and software.
16 °	Final exam

6. METHODOLOGY

The course is developed through theoretical - practical activities, emphasizing applications in industry and services.The students were divided into teams of 3 students will develop two computational work. During the lab sessions CLIPS non procedural language or a variant of LISP will study and advancement of computational work will be evaluated.

7. EVALUATION

Final average (PF) is determined as follows: 
PF = 0.0125 (CL1 + CL2 CL3 + + CL4) + 0.01 (TB1 + TB2) + 0.15 + 0.30 * LA * (EA + EB) 
Where: 
CLx: Reading Controls (CL1, CL2, CL3 and CL4) 
TB1: Group Work (Intelligent Games hombra - Machine)

TB2: Group Work (Expert Systems) 
EA: Partial Review 
EB: Final Exam 
LA: Laboratory

The student may replace the exam partial or final if it was unable to provide any of these tests.

Just be evaluated students displaying 70% or more of assistance, ie 5 fouls are automatically removed from the course.

8. REFERENCES

     [1] STUART, RUSSELL; PETER, Norvig 
2010 Artificial Intelligence: a modern approach. Ed. Prentice Hall. 
ISBN 0-13-103805-2 

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

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

    [4] DAVID, MAURICIO 
2009 Notes of artificial intelligence 

    [5] BONIFACIO, MARTIN; ALFREDO, SANZ 
2002 Neural networks and fuzzy systems. Ed. Alfaomega. 
ISBN 84-7897-466-0 

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

   [7] JOSÉ M. PALMA, M. ROQUE MARIN 
2008 Artificial Intelligence, technical methods and applications. Ed. McGraw-HIll. 
ISBN 978-84-484-5618-3 

    [8] R. ROW JOSE, VICTOR J. MARTINE. 
2000 Artificial neural networks, foundations, models and applications. 
Ed Alfaomega -. Branch. 
ISBN 978-84-484-5618-3 

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

    [10] CAMPELO Ruy; MACULAN NELSON 
1994, Algorithms and Herísticas. Ed. Fluminense Federal University. 
GLOVER Fred; KOCHENBERGER Gary A. 

    [11] 2003 HandBook of MEtaheuristic. Kluwer International Series.