artificial intelligent

ALI AKRAM SABERID : P71082

SUBJECT : INTELLIGENT URBAN TRAFFIC CONTROL SYSTEM

ARTIFICIAL INTELLIGENT

INTRODUCTION

Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. It is also an academic field of study. Major AI researchers and textbooks define the field as "the study and design of intelligent agents", where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. It as "the science and engineering of making intelligent machines".

NEURAL NETWORK

Neural network" redirects here. For networks of living neurons, see Biological neural network.Neural Networks can be loosely separated into Neural Models, Network Models and Learning Rules. the earliest mathematical models of the Neuron pre-date Mcullock and Pitts who developed the first Network models to explain how the signals passed from one neuron to another within the network. When you hear of a network being described as a feed forward or feedback network, they are describing how the network connects neurons in one layer to neurons in the next. Weiners work allowed Mculloch and Pitts to describe how these different connection types would affect the operation of the network.

Hopfield: A Hopfield network is a fully connected network. A unit receives input from all other units. There is no distinction between input units, hidden units and output units. When an input pattern is presented, all units obtain their initial state from the input pattern.

NEURAL NETWORK

Boltzmann divides all network nodes into three groups: input nodes, output nodes, and hidden nodes

Multi-layered network is a feed forward network. Three or more layers of artificial neurons are used with one layer representing input data and one layer representing the corresponding output.

Adaptive Resonance Theory The term "resonance" refers here to the so called resonant state of the network in which a category prototype vector matches the current input vector close enough so the orienting subsystem will not generate a reset signal to the second layer.

NEURAL NETWORK

in the computer science field of artificial intelligence, genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.

Genetic algorithms find application in bioinformatics, phylogenetic, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields.

GENETIC ALGORITHM

Initially many individual solutions are (usually) randomly generated to form an initial population. The population size depends on the nature of the problem, but typically contains several hundreds or thousands of possible solutions. Traditionally, the population is generated randomly, allowing the entire range of possible solutions (the search space). Occasionally, the solutions may be "seeded" in areas where optimal solutions are likely to be found.

INITIALIZATION OF GENETIC ALGORITHM

FLOW CHART

Before a genetic algorithm can be put to work on any problem, a method is needed to encode potential solutions to that problem in a form so that a computer can process.

Common approaches are: Binary Encoding : every chromosome is a string of 0 or 1

• Permutation Encoding : every chromosome is a string of

numbers that represent position in a sequence

• Tree Encoding : a tree structure represents the chromosome

• Value Encoding : every chromosome is a sequence of some

values (real numbers, characters or objects)

ENCODING

Expert Systems are computer programs that are derived from a branch of computer science research called Artificial Intelligence (AI). AI's scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior. It is concerned with the concepts and methods of symbolic inference, or reasoning, by a computer, and how the knowledge used to make those inferences will be represented inside the machine.

The term intelligence covers many cognitive skills, including the ability to solve problems, learn, and understand language; AI addresses all of those. But most progress to date in AI has been made in the area of problem solving.

EXPERT SYSTEMS

Every expert system consists of two principal parts: the knowledge base; and the reasoning, or inference, engine.

The knowledge base of expert systems contains both factual and heuristic knowledge. Factual knowledge is that knowledge of the task domain that is widely shared, typically found in textbooks or journals, and commonly agreed upon by those knowledgeable in the particular field.

Heuristic knowledge is the less rigorous, more experiential, more judgmental knowledge of performance. In contrast to factual knowledge, heuristic knowledge is rarely discussed, and is largely individualistic. It is the knowledge of good practice, good judgment, and plausible reasoning in the field. It is the knowledge that underlies the "art of good guessing."

THE BUILDING BLOCKS OF EXPERT SYSTEMS

In conventional computer programs, problem-solving knowledge is encoded in program logic and program-resident data structures. Expert systems differ from conventional programs both in the way problem knowledge is stored and used.

DISTINGUISHING FEATURES

Expert systems are especially important to organizations that rely on people who possess specialized knowledge of some problem domain, especially if this knowledge and experience cannot be easily transferred. Artificial intelligence methods and techniques have been applied to a broad range of problems and disciplines, some of which are esoteric and others which are extremely practical.

UTILITY OF EXPERT SYSTEMS

A rule-based, expert system maintains a separation between its Knowledge-base and that part of the system that executes rules, often referred to as the expert system shell. The system shell is indifferent to the rules it executes. This is an important distinction, because it means that the expert system shell can be applied to many different problem domains with little or no change.

ADVANTAGES OF RULE-BASED SYSTEMS

RULE-BASED SYSTEMS

Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. Compared to traditional binary sets (where variables may take on true or false values), fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when linguistic variables are used, these degrees may be managed by specific functions. Irrationality can be described in terms of what is known as the fuzzjective.

FUZZY LOGIC

FUZZY INFERENCE SYSTEM

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