Syllabus

1. GENERAL SPECIFICATIONS

Course : Artificial Intelligence
Code of Course : 207008
Course duration : 17 weeks
Teaching Methodology : Technical - experimental
Hours per week : Theory: 3h – Laboratory: 2h
Nature: Professional Training
Number of credits : Four (04)
Prerequisites : 205007 – Operations Research I
Academic Semester : 2012 – I
Coordinator:

2. ABSTRACT

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

3. GENERAL OBJECTIVE

Students will gain knowledge in the area of Artificial Intelligence in general and they will develop basic aspects in the development of intelligent games and expert systems, and its applicationn in the resolution of problems in the areas of industry and services.

4. SPECIFIC OBJECTIVES

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 usingartificial intelligence techniques.
5. Understand what they are expert systems and know when to use them.
6. Knowing which is the Knowledge Engineering and a method for developingknowledge-based systems
7. Evaluate the quality of expert systems solution.
8. Design and develop expert systems based on different inference engines (methodschaining), considering quality criteria.
9. Understand the concepts of machine learning and heuristic, its importance and its applications in industry and services.

5. ANALYTICAL CONTENT PER WEEKS

Week	Theme	Theory Works	Laboratory Works
1	Classification of Algorithmc 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.
2	Principles of Artificial Intelligence Definition of Artificial Intelligence. Intelligent machine. Difference between operating systems and intelligent systems. Applications in industry and services (robotics, planning, waste management). TuringTest. References: [1] Chapter 1, [2] Chapter 1, [9] Chapter 1.
3	Representation of human machine gambling problems as search in a state space Definition of AI problems as search problems in a state space. Representation of human machine gambling problems. References: [1] Chapters 3, [3] Chapter 2, [4] Chapter 3.
4	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] Chapter 5, [9] Chapters
5	Search methods for human-machine games Human algorithm game - machine. Strategies machine game: no deterministic best-first, min-max difference and betterprofits. Algorithm min-max and alpha-beta. References: [1] Chapter 5, [2] Chapter 6, [3] Chapters 4, [4] Chapters 6, [9] Chapter 12.
6	Principles 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: [6] Chapter 1
7	Knowledge Engineering Introduction. Acquisition of knowledge. CommonKADS methodology. Design of Expert Systems (ES). Life cycle of an SE. References: [6] Chapters 6, [7] Chapter 19.
8	Partial Exam
9	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.
10	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.
11	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: [1] Chapters 6 and 8, [2] Chapter 7 [6] Chapter 3, [7] Chapter 3
12	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. References: [4], [7] Chapter 21.
13	Introduction to Machine Learning. 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. References: [5] Chapter 1, [8] Chapter 1
14	Introduction to heuristic and meta-heuristics The combinatorial optimization problem. Complexity of combinatorial problems. Concepts of heuristics and meta-heuristics. Heuristic algorithms vs. exact algorithms. Heuristics and meta-heuristics. Combinatorial optimization problems in industry and services. References: [10], [11].
15	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.
16	Final Exam.
17	Substitute Exam (Only for those who did not give partial or final exam).

6. METHODOLOGY

The course is developed through theoretical activities - practices, emphasizingapplications in industry and services. The students were divided into teams of 3 going to develop two computational works. During the theory sessions will discuss the proposed problem solving. During the laboratory sesciones readings as well as evaluate the progress of the work computer.

7. EVALUATION

The final average (PF) is determined as follows:
PF = 0.025 (CL1 + CL2 + CL3 + CL4) + 0.075 (TB1 + TB2) + 0.15 * 0.30 * LA + (EA +EB)
Where:
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.Students will be evaluated only showing 70% or more assists.

8. BIBLIOGRAPHY

STUART, RUSSELL, PETER, Norvig
2010 Artificial Intelligence: A Modern Approach (3rd Edition). Prentice Hall Ed.
ISBN-13: 978-0136042594
PATRICK, WINSTON
Artificial Intelligence 1984. Ed Addison-Wesley
ISBN 0-201-51876-7
ELAINE RICH
Artificial Intelligence 1988. Ed McGraw-Hill
ISBN 0-07-450364-2
DAVID, Mauritius
Notes in Artificial Intelligence 2000.
BONIFACE, MARTIN, ALFREDO, SANZ
2002 Neural Networks and Fuzzy Systems. Ed Alfaomega
ISBN 84-7897-466-0
Giarratano RILEY
2001 Expert systems, principles and programming. Ed Thomson Science
ISBN 970-686-059-2
The required readings will be provided by the course instructor