Artificial Intelligence 2018 | Hugo Vega UNMSM Faculty of Systems and Informatics Artificial Intelligence

Introduction
Artificial intelligence is an area of computer science that aims to develop the practical and theoretical foundations for the development of computing systems that exhibit intelligent characteristics and that usually correspond to intractable problems. Many problems that occur in industry, services and entertainment correspond to intelligent problems, and their solution is becoming increasingly essential in organizations due to the competitiveness demands that increase every day.
Among the various types of intelligent systems are human-machine games, knowledge-based systems, optimization systems and machine learning.
In the present course, An introduction to artificial intelligence and its applications in industry is given, services and entertainment, and it shows how technologies based on artificial intelligence can create value and make organizations more competitive. Its applications in software engineering will also be shown.
The design and implementation of human-machine games and knowledge-based systems is addressed in greater depth.
Course achievement
At the end of the course, the student will acquire general knowledge in the field of artificial intelligence, will design and implement human-machine competition games based on artificial intelligence and knowledge-based systems, making clear and precise use of search techniques in a state space and the CommonKADS methodology.
Learning Units
30 h/2-7
15 h/11-13
10 h/14-15
Decision problems, location and optimization.
Description of some NP-hard problems.
Class No. 1
Intelligent machine.
Applications in industry and services (robotics, planning, waste management).
Turing Test.
Class No. 2
three,4
Representation of human game problems – machine.
Class No. 3
Class No. 4
Binary Trees Lisp
Recursion Exercises
Laboratory No. 1
Laboratory No. 2
The impossibility of using shortest-path methods.
The concept of blind and informed search methods.
The state tree. Blind methods: breadth, depth, non-deterministic.
Class No. 5
Laboratory No. 3
6
Class Roko's Basilisk No. 6
Intelligent System – Arduino
Intelligent System (Files)
Laboratory No. 4
Human game algorithm – machine.
Machine game strategies: non-deterministic, best first, min-max and best utility difference.
Min-max and alpha-beta algorithm.
8
Taxonomy and applications of expert systems.
Requirements for the development of expert systems and advantages of using expert systems.
Some knowledge-based problems.
Class No. 8
Laboratory No. 5
Life cycle of an ES.
Class No. 9
Project Progress
Laboratory No. 6
Exercise in Prolog
Construction of the facts base and knowledge base.
Knowledge representation structures (inference rules, frames, objects, ontologies, metadata, thesaurus).
Class No. 10
The inference engine.
Matching techniques, the RETE algorithm.
Conflict resolution techniques.
Laboratory No. 8
Quality of an expert system.
Validation of intelligent systems, Quantitative methods of validation.
Efficiency and error of expert systems. Review of the functionality of the ES from the 2nd assignment.
Tasks: Exercises on quality and validation of ES, Validate the proposed system from the 2nd assignment.
Resolution of 3rd Laboratory Practice
Laboratory No. 9
Expert system vs machine learning.
Learning techniques and development phases of machine learning.
Applications of machine learning in industry and services.
The problem of combinatorial optimization.
Concepts of heuristics and meta-heuristics.
Exact algorithms vs heuristic algorithms.
Heuristic and meta-heuristic techniques.
Laboratory No. 10
Presentation of computational assignments
The students will demonstrate their skills regarding the development of expert systems and their applications in the industry and service sectors. The students will present a report and a software program..
Project Article Closing
Project Last (Files)
Laboratory No. eleven
sixteen