Inteligencia Artificial IA7700-T M.C. Juan Carlos Olivares Rojas email@example.comSlide 2
Agenda Repaso básico de IA sobre: Introducción Agentes lógicos Métodos de Búsqueda Prolog Lisp PlanificaciónSlide 3
Basic Concepts What\'s the diference bewtween Artificial Intelligence (AI) and Human Intelligence? All the sucessfully AI Systems depend on human learning and experience. The greater part of the AI Systems can be costructed just when the human knowledge can be expresed in effortlessly frame (for case: if x then y ).Slide 4
Basic Concepts AI Systems augment human specialists, however never can\'t substituting either "taken" a large portion of human intlligence. AI Systems don\'t have judgment skills and generallity of individuals. Human Intelligence are extremely mind boggling for processing.Slide 5
Basic Concepts If an issue can not be depicted, then can not be customized Human Intelligence have these elements: Reasoning. Conduct. Utilization of Metaphores and Analogies. Ideas Creating and Use.Slide 6
Problem Make a Java Program which figure if a number give for the client is a Perfect Number or not. What are the means for tackling this issue?Slide 7
Inteligence Capacity to arrangement all clasess of issues Intelligence is exceptionally subjective. "Knowledge Distinguished man of creatures" AI is an interdiciplinary science which includes phylosophy, matemathics, science, hardware, and so on,Slide 8
Turing Test Alan M. Turing characterized in 1950 one structure to check if a machine is shrewd or not. Turing test comprise to set two human and one machine in a dim room. The people and the machine are not noticeable between their. One human must act like an Interviewer posing a few questions to alternate members.Slide 9
Turing Test Turing Test is passed when the questioner can not recognized the answer between the human and the machine. The new AI frameworks required the discernment sense to breeze through the test.Slide 10
AI Genesys Martin Minsky did cotributions to characterize cerebrum models in PCs. ELIZA of Joseph Weizenbaum and JULIA of Mauldin were the main AI Systems with Intelligent Dialagues. The main AI Systems were advancement for taking care of a few issues like chess.Slide 11
Génesis de la IA In 1956 John McCarthy and Claude Shanon distributed "Automata Studies" where characterized the Automata Theory. In 1956 John McCarthy characterized the AI idea, motivation behind why he is viewed as the AI Father. The AI history is extremely old. The greeks were the first to utilize rationale to take care of a great deal of issues.Slide 12
AI Genesys In 1965 Chomsky characterized the Formal Languages Theories. McCulloh and Pits in 1943 characterize the relations amongst neurons and basic computational components. In 1962 Rosenblatt characterized the Perceptron and the Neuronal Networks Teories.Slide 13
1.2 Applications Solution Search Expert System Natural Language Recognition Pattern Recognition Robotic Machine Learning Logic Games Neuronal Networks Genetic Algorithms Virtual RealitySlide 14
Maze Problem Additional Homework: Study Graph Theory, Discret Mathematics, Computing Theory (Compilers). Clusters in some abnormal state programming dialects. How a man in a labyrinth can be way out without lost? Are there an ideal answer for the issue?Slide 15
Solution Search The inquiry term appliend in AI, it\'s not mean locate a particular data piece in an information reporsitory, this term infers to acquire the best answer for an issue. For example: Finding the most brief way between two urban areas, or the famus "Voyaging Sales Problem" (TSP). This is a NP-Complete (Not Polinomal) Problem.Slide 16
Expert Systems They were the main AI comercial item sucessfully. These Systems let to present some data in a particular learning territory into a PC (information database), they act like a human master. These Systems reenact human thinking by applicating especific information and surmisings.Slide 19
Natural Language Processing It\'s an unpredictable issue. For instance (in spanish): "Thoughts verdes descoloridas duermen furiosamente", "Thoughts furiosamente verdes descoloridas duermen".Slide 20
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]"Slide 21
Artificial Vision It\'s a use of patter acknowledgment, this region have a great deal of use, for example, Medical Diagnostic Automatic Signal Processing Automatic Industrial Product Automatic Vigilance Systems OCR (Optical Character Recognition)Slide 22
Robotic This science suggests the ideas of discernment, movement (spatial thinking), arranging . The primary issue self-governing robots are communicating with the human-world, since exists numerous hindrances startling occasions and dinamic situations.Slide 23
Learning This zone contemplates the path in how PCs can acquire new information to take care of an issue. In this sense, learning intends to make a PC which can profit for the experience got.Slide 24
Games AI is connected in recreations to give more authenticity and unpredictability. Additionally AI gives the "Material science". The n-rulers issue comprise in putting n chess rulers on a n×n chessboard with the end goal that none of them can catch some other utilizing the standard chess ruler\'s moves.Slide 25
Games Activitie: Obtain a Solution in a sheet of paper for a 5x5 chessboard. Initial 100, Second 80, Third 60 pts.Slide 26
Genetic Algorithms It\'s a computational procedure enlivened in natural models which are utilized to acknowledge eficient look in spatial arrangement exceedingly enormous and complex. Hereditary Algorithms are adaptative techniques which can used to execute pursuits and streamlining issues. This has given the formation of development zones, for example, transformative calculation and swarm figuring calculations that depend on occasions of nature.Slide 27
The Game of Life The Game of Life, likewise referred to just as Life, is a cell machine concocted by the British mathematician John Horton Conway in 1970. It is the best-known case of a cell machine. The "game" is really a zero-player diversion, implying that its development is controlled by its underlying state, requiring no contribution from human players. One collaborates with the Game of Life by making an underlying arrangement and watching how it advances.Slide 28
The Game of Life The universe of the Game of Life is an endless two-dimensional orthogonal framework of square cells, each of which is in one of two conceivable states, live or dead. Each phone associates with its eight neighbors, which are the phones that are straightforwardly evenly, vertically, or corner to corner nearby. At every progression in time, the accompanying moves happen:Slide 29
The Game of Life Any live cell with less than two live neighbors kicks the bucket, as though by requirements brought on by underpopulation. Any live cell with more than three live neighbors bites the dust, as though by congestion. Any live cell with a few live neighbors lives, unaltered, to the people to come.Slide 30
Any dead cell with precisely three live neighbors turns into a live cell.Slide 31
The Game of LifeSlide 32
The Game of Life Find an underlying arrangement with under 16 live cells. The best aproximation wins 100, second 80, third 60 focuses. Play the amusement at: www.bitstorm.org/gameoflife/Slide 33
Virtual Reality It\'s a standout amongst the latest utilizations of AI. It\'s comprise in the development of projects which achive to trick the human detects, make it belive that we are gliding, running or flying in a plane. This application has been utilized as a part of a fligth test system for pilots, space travelers and drivers.Slide 34
Intelligent Systems and Learning Most of the genuine framework say that they are intelligents ("brilliant"). On the off chance that an application can take self-ruling choices in an ongoing in independet structure, it\'s viewed as savvy. The fundamental component of this frameworks are the "versatility" like sparing vitality.Slide 35
Intelligent Systems and Learning The most imperative component of an Intelligent System are the best approach to speaking to the information, the path in which the data is retrived and the route in which adquire new (learning). The representation courses ("explicitation") of information are various and it impacts in the retrival informtion and learning ways.Slide 36
Intelligent Systems and Learning Always that a model is produced it has two represetation: consistent and physical. This representations need "mapping" to cooperating. When we have a genuine issue, this need to mapping in a PC composition for working in a computational framework.Slide 37
Intelligent Syst. what\'s more, Knowledge Tacking back to the Maze Problem ¿How can be speak to this model and the information? It can be spoken to with a framework, chart, limited state machine, and so forth. Additionally it must standards for play this amusement. On the off chance that we don\'t have the two representations we can not comprehend and take in the diversion.Slide 38
Intelligent Systems and Knowledge all in all, learning s characterize by laws and specific dialects. Dialects characterize rules. The same learning is organized in diferents represtentation, for example, database, semantic systems, outlines, reasonable maps, and so forth., however after all it must have the same importance (semantics).Slide 39
Homework and Activity: programming the Game of Life utilizing a High-Level dialect with a 8x8 framework. The project can be in content mode and the client just can set the underlying arrangement. Utilizing a BitMap Matrix (0 and 1 values) Activity: programming the right-hand heuristic for settle a labyrinth presented for an utilized.Slide 40
Homework and Activity ExtraPoint: programming a labyrinth generator. The labyrinth just have one info and one yield (It can be the same that information). The labyrinth era must be ordened by a calculation utilizing a spatial arrangement searc
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