AI: Basic Concepts M.C. Juan Carlos Olivares Rojas February, 2009

Outline 1.1 Basic Concepts 1.2 Applications 1.3 Intelligent Systems and Learning 1.4 Semantic Networks 1.5 Description and Match Method

Outline 1.6 Analogy Problems 1.7 Abstraction Recognition 1.8 Knowledge Interpretation

Space Oddisey 2001 What are the principal features about Artificial Intelligence of this movies? Quiz 1 ¿What’s the name of the computer? ¿What are the nam of the human travelers in the spaceshift? ¿When te computer was elaborated? ¿Whose write the movie script?

Basic Concepts What’s the diference bewtween Artificial Intelligence (AI) and Human Intelligence? All the sucessfully AI Systems are based on human knowledge and experience. Most of the AI Systems can be costructed only when the human intelligence can be expresed in easily form (for instance: if x then y).

Basic Concepts AI Systems extend human experts, but never can’t substituting either “taken” most of human intlligence. AI Systems don’t have common sense and generallity of human beings. Human Intelligence are very complex for computing.

Basic Concepts If a problem can not be described, then can not be programmed Human Intelligence have these features: Reasoning. Behavior. Use of Metaphores and Analogies. Concepts Creating and Use.

Problem Make a Java Program which calculate if a number give for the user is a Perfect Number or not. What are the steps for solving this problem?

Inteligence Capacity to solution all clasess of problems Intelligence is very subjective. “Intelligence Distinguished man of animals” AI is an interdiciplinary science which involves phylosophy, matemathics, biology, electronics, etc,

Turing Test Alan M. Turing defined in 1950 one form to check if a machine is intelligent or not. Turing test consist to set two human and one machine in a dark room. The humans and the machine are not visible between their. One human must act like an Interviewer asking some questions to the other participants.

Turing Test Turing Test is passed when the interviewer can not distinguished the answer between the human and the machine. The new AI systems required the perception sense to pass the test.

AI Genesys Martin Minsky did cotributions to define brain models in computers. ELIZA of Joseph Weizenbaum and JULIA of Mauldin were the first AI Systems with Intelligent Dialagues. The first AI Systems were development for solving some problems like chess.

Génesis de la IA In 1956 John McCarthy and Claude Shanon published “Automata Studies” where defined the Automata Theory. In 1956 John McCarthy defined the AI concept, reason why he is considered the AI Father. The AI history is very old. The greeks were the first to use logic to solve a lot of problems.

AI Genesys In 1965 Chomsky defined the Formal Languages Theories. McCulloh and Pits in 1943 define the relations between neurons and simple computational elements. In 1962 Rosenblatt defined the Perceptron and the Neuronal Networks Teories.

Homeworks Delivery: 05/02/09 Activitie 1: Make a timeline with the history of AI. 40% The timeline must be doing with a special software. The timeline must include antoher events

Homeworks Activitie 2: Research about cognitive science. 40% What are the cognitive science? What are the most important things in cognitive science Remember 20% are for the work format

1.2 Applications Solution Search Expert System Natural Language Recognition Pattern Recognition Robotic Machine Learning Logic Games Neuronal Networks Genetic Algorithms Virtual Reality

Maze Problem Additional Homework: Study Graph Theory, Discret Mathematics, Computing Theory (Compilers). Arrays in some high-level programming languages. How a person in a maze can be exit without lost? Are there an optimal solution for the problem?

Solution Search The search term appliend in AI, it’s not mean find a specific information piece in a data reporsitory, this term implies to obtain the best solution for a problem. For instance: Finding the shortest path between two cities, or the famus “Travelling Sales Problem” (TSP). This is a NP-Complete (Not Polinomal) Problem.

TSP

Expert Systems They were the first AI comercial product sucessfully. These Systems let to introduce some information in an specific knowledge area into a computer (knowledge database), they act like a human expert. These Systems simulate human reasoning by applicating especific knowledge and inferences.

Natural Language Processing It’s a complex problem. For example (in spanish): “Ideas verdes descoloridas duermen furiosamente”, “Ideas furiosamente verdes descoloridas duermen”.

Natural Language Processing “El banco cierra a las 3:00” “Las almejas están listas para comer” “Las almejas están listas para [ser] comidas [por nosotros]”

Artificial Vision It’s an application of patter recognition, this area have a lot of application such as: Medical Diagnostic Automatic Signal Processing Automatic Industrial Product Automatic Vigilance Systems OCR (Optical Character Recognition)

Robotic This science implies the concepts of perception, motion (spatial reasoning), planning. The main problem autonomous robots are interacting with the human-world, because exists many obstacles unexpected events and dinamic environments.

Learning This area studies the way in how computers can obtain new knowledge to solve a problem. In this sense, learning means to make a computer which is able to benefit for the experience obtained.

Games AI is applied in games to give more realism and complexity. Also AI gives the “Physics”. The n-queens problem consist in putting n chess queens on an n×n chessboard such that none of them is able to capture any other using the standard chess queen's moves. Activitie: Obtain a Solution in a sheet of paper for a 5x5 chessboard. First 100, Second 80, Third 60 pts.

Genetic Algorithms It’s a computational technique inspired in biological models which are used to realize eficient search in spatial solution highly huge and complex. Genetic Algorithms are adaptative methods which can used to implement searches and optimization problems. This has given the creation of emergence areas such as evolutionary computation and swarm computing algorithms that rely on events of nature.

The Game of Life The Game of Life, also known simply as Life, is a cellular automaton devised by the British mathematician John Horton Conway in It is the best-known example of a cellular automaton. The "game" is actually a zero-player game, meaning that its evolution is determined by its initial state, needing no input from human players. One interacts with the Game of Life by creating an initial configuration and observing how it evolves.

The Game of Life The universe of the Game of Life is an infinite two-dimensional orthogonal grid of square cells, each of which is in one of two possible states, live or dead. Every cell interacts with its eight neighbours, which are the cells that are directly horizontally, vertically, or diagonally adjacent. At each step in time, the following transitions occur:

The Game of Life Any live cell with fewer than two live neighbours dies, as if by needs caused by underpopulation. Any live cell with more than three live neighbours dies, as if by overcrowding. Any live cell with two or three live neighbours lives, unchanged, to the next generation. Any dead cell with exactly three live neighbours becomes a live cell.

The Game of Life

Find an initial solution with under 16 live cells. The best aproximation wins 100, second 80, third 60 points. Play the game at:

Virtual Reality It’s one of the most recent applications of AI. It’s consist in the construction of programs which achive to fool the human senses, make it belive that we are floating, running or flying in an airplane. This application has been used in a fligth simulator for pilots, astronauts and drivers.

Intelligent Systems and Learning Most of the actual system say that they are intelligents (“smart”). If an application can take autonomous decisions in a real time in independet form, it’s considered intelligent. The main feature of this systems are the “adaptability” like saving energy.

Intelligent Systems and Learning The most important feature of an Intelligent System are the way to representing the knowledge, the way in which the information is retrived and the way in which adquire new knowledge (learning). The representation ways (“explicitation”) of knowledge are diverse and it influences in the retrival informtion and learning ways.

Intelligent Systems and Learning Always that a model is developed it has two represetation: logical and physical. This representations need “mapping” to working together. When we have a real life problem, this have to mapping in a computer schema for working in a computational system.

Intelligent System and Knowledge Tacking back to the Maze Problem ¿How can be represent this model and the knowledge? It can be represented with a matrix, graph, finite state machine, etc. Also it must rules for play this game. If we don’t have the two representations we can not understand and learn the game.

Intelligent Systems and Knowledge In general, knowledge s define by laws and particular languages. Languages define rules. The same knowledge is structured in diferents represtentation such as database, semantic networks, frames, conceptual maps, etc., but after all it must have the same meaning (semantics).

Homework and Activity Homework: conceptual maps. 10% Format 10% Basic Concepts 40% Conceptual Map for a student well-know topic 40% Conceptual Map for a any AI topic. Note: the conceptual maps must be development in a computational tool.

Homework and Activity Activity: programming the Game of Life using a High-Level language with a 8x8 matrix. The program can be in text mode and the user only can set the initial configuration. Using a BitMap Matrix (0 and 1 values) Activity: programming the right-hand heuristic for solve a maze introduced for a used.

Homework and Activity ExtraPoint: programming a maze generator. The maze only have one input and one output (It can be the same that input). The maze generation must be ordened by a algorithm using a spatial solution search. An easy way is put the input and output, generate one path (the correct path) and later generate other incorrect paths, beginning of the correct path.

Maze Generator We must try to don´t generate a loop

Cellular Automata It’s a discrete model studied in computing, mathematics, biology and microstructure modeling. It consists of a regular grid of cells, each in one of a finite number of states. The grid can be in any finite number of dimensions. Time is also discrete, and the state of a cell at time t is a function of the states of a finite number of cells (neighborhood) at time t − 1.

Cellular Automata These neighbors are a selection of cells relative to the specified cell, and do not change (though the cell itself may be in its neighborhood, it is not usually considered a neighbor). Every cell has the same rule for updating, based on the values in this neighbourhood. Each time the rules are applied to the whole grid a new generation is created.

Cellular Automata Rule 30 Pattern State Rule 110 Pattern State

Semantic Networks They are other simple form to explicity knowledge, They are conformed by graphs which coding knowledge in a taxonomic form. Nodes represent categories and Edges represents the relations between this categories. There are two types of special relatinoships: Is-A y la Have-A.

Semantic Networks We can access throught of each concepts to infer knowledge. The scripts are other way to represent knowledge. They are composed by components called slots, these are a set of elements concept-values. Scripts are more easily to ntroduce than mind maps.

Semantic Networks

Script Script Example: Printers Subset_of: Office_Machine Superset_of: {Laser_Printer, Inject_Printer} Feed_Source: Door_Socket Author: Juan_Perez Date: 07_January_2008

Onthologies Other way to represent knowledge with a lot of use recently is Onthology, It’s consist of relations between distinct concepts like definitions. Onthologies can be represented throught languages such as XML. Knowledge representation has a great importance this is the reason because actually we talk about Knowledge Engineering.

Semantic Networks Onthologies act like a dictionary. Some elements like agents used this information to represent and retrieve knowledge. Frames are structure used to represent values, restricctions, process, relation, etc. Frames represent with tuples one propertie of an object. Object-Oriented Programming was originated by Frames.

Concept Mind

Onthology

Activity Represent one object (one diferent per student, e.g. Wireless Network Card, Telephone, Cow, etc.) and all its features with an Ontology, Script and Frame.

The Description and Match Method It’s used for AI problem solving and It’s one the most basic method. The first step consists to identified all features of an object. Later, It realice a seach in a well-define set of objects. It needs two very import methods: the extractor and evaluator of knowledge.

The Description and Match Method When the match process is doing, one posibility is the object don’t be the same pattern in the knowledge Database. This is the reason because We need a Similarity Function. For Example (In Spanish): AMOR Love a person or thing for over all things Word composeb dy 4 characters: ‘A’, ‘M’, ‘O’ and ‘R’ yuxtapuestos

The Description and Match Method AMOR = AMORExact Macth AMOR = ROMA 0% similarity but contains for characters Amor = AMOR 25% similarity, contains all character but in uppercase Amor = Cariño 0% similarity but the same meaning Amor = Amar 75% it’s a consequence

The Description and Match Method Circle Description: Figure formed by al the points which distance are equidistant of the center point in an angle of 0 a 360 grades. Properties: Center (point) Diametrer (twice radio) Areas

The Description and Match Method = = = = 100% Similarity ?% Have the same form but diferent size and color ?% Have the same high but diferent width ?% Have the same color

The Description and Match Method It`s used in multiples branches such as: – Digital Fingerprint Recognition – Voice Recognition – Natural Language Recognition – Software Requirement Validation – Etc. We must represent in a correct form the knowledge if We can compare.

The Farmer, Fox, Goose and Wheat Problem A farmer wants to move himself, a silver fox, a fat goose, and some tasty grain across a river, from the west side to the east side. Unfortunately, his boat is so small he can take at most one of his possessions across on any trip. Worse yet, an unattended fox will eat a goose, and an unattended goose will eat grain, so the farmer must not leave the fox alone with the goose or the goose alone with the grain. What is he to do?

The Farmer, Fox, Goose and Wheat Problem Farmer Fox Goose Wheat Farmer Fox Goose Wheat ¿Se puede utilizar el método de descripción y pareamiento?

The Farmer, Fox, Goose and Wheat Problem Farmer Fox Goose Wheat Fox Wheat Farmer Goose Farmer Fox Wheat Goose Wheat Farmer Fox Farmer Goose Fox Wheat Farmer Fox Goose Wheat Fox Goose Wheat Farmer Fox Goose Wheat Farmer Fox Goose Farmer Goose Wheat Fox Goose Wheat Farmer Goose Wheat Fox Farmer Wheat Farmer Fox Goose Fox Farmer Goose Wheat Fox Goose Wheat Farmer Fox Goose Wheat

Activity In a Software Development Company 5 programers implement the same algorithms obtained the follow results: ProgrammerLOCReturnFunction Call

Activity The enterprise needs to know how are the best pair (pair programing). For trying to solve this problem We need to define a similarity function such as: s(v, w)=|p1-q1| + |p2-q2| + |p3-q3| Where: v and w are programmers represent in the form of (p1, p2, p3)

Activity pi is a propertie We also need a criteria for similarity in this case consider the lower punctuation as the best solution. Programming the solution to obtain the best pair. Programming the solution to obtain the best pair in a specific propertie

Activity If We changed the criteria in where LOC are more important 60% than the other properties, how must be the new similarity function? If We need one group with the 3 best programmer, how must be the similarity function?

Analogy Problems It’s other form to problem solving tha it’s used in AI. Analogy is a special type of relation that define how are objects represented los objetos de una categoría y como obtener sus predecesores y antecesores inmediatos. Generalmente se habla de análogo cuando se tiene el mismo tipo de relación aun cuando sean entidades diferentes.

Problemas de Analogías Alguna vez nos hemos preguntado ¿por qué en la mayoría de los exámenes de admisión generalmente son más importantes que los de conocimientos? Por que en la mayoría de los casos el conocimiento de cierta forma se puede adquirir pero la forma de aprender y razonar es sumamente complicado. En muchos casos son más importantes las reglas que el conocimiento.

Problemas de Analogías En matemáticas y en el área de programación se utiliza mucho la analogía para resolver problemas. De acuerdo con Polya, para resolver problemas se necesita de los siguientes pasos: 1) Comprender el problema 2) Concebir un plan 3) Ejecutar el plan y, 4) Examinar la solución.

Problemas de Analogías AB C ¿Cómo quedarían D y 5?

Problemas de Analogías ¿Qué problemas se presentan con la Abstracción de la Figura D o bien de la Figura 3? La resolución de problemas por analogía tiene como base cierto conocimiento previo en ocasiones difícil de obtener. A B C 12

Reconocimiento de Abstracciones A lo largo de esta presentación se ha podido comprobar que prácticamente el problema está resuelto si el problema está descrito. El reconocimiento de abstracciones es un concepto muy subjetivo dado que éstas son combinaciones de estados mentales y eventos. Los SI se basan fundamentalmente en reglas ECA (Evevento-Condición-Acción)

Reconocimiento de Abstracciones Generalmente respondemos a estímulos (eventos), y en base a ellos vemos cuales son importantes para nosotros y nos comportamos de cierta manera. Para lo que a una persona le representa algo para otra representa cosas totalmente distintas. La abstracción permite llegar a cierto tipo de conclusiones y preguntas resueltas.

Interpretación del Conocimiento La interpretación del conocimiento, es decir la utilización de ese conocimiento es un factor muy importante que aun la IA no ha podido definir bien. El conocimiento se puede interpretar de muchas formas y sus áreas de aplicación son diversas. Existen muchas corrientes filosóficas que le tratan de dar sentido al conocimiento: empirismo y racionalismo científico.

Interpretación del Conocimiento Se pretende que las reglas y hechos (base de conocimientos) permitan resolver problemas y que a su vez de la resolución de estos problemas se obtenga nuevos conocimientos.

Homework Make a survey (Caracterization) about the frecuencie of each character in Spanish Alfabet. For example, in % how many ‘c’ appears. This surve will aplied on Inteligent Cryptoanalisys System. Research about cryptograph system by transposition and substitution algorithms.

Bibliografía Decker, R. y Hirshfield, S. (2001). Máquina Analítica. Introducción a las Ciencias de la Computación con Uso de Internet, Thomson, México. Capítulo 9 Inteligencia Artificial pp Hernández, V. (2007). Mapas Conceptuales La gestión del Conocimiento en la Didáctica. Segunda Edición, México: Alfaomega.

Bibliografía G. Polya, (1982), “Cómo Plantear y Resolver Problemas”, traducción al español de “How to Solve It”, Ed. Trillas, México, 1982, ISBN: Montes, M. y Villaseñor L. (2008) Fundamentos de Inteligencia Artificial Métodos básicos de solución de problemas, Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, México.

Bibliografía Winston, P. (1992) Artificial Intelligence, 3ra. Edición, Addison-Wesley.

Questions?