Download Artificial Intelligence and Expert Systems and more Study notes Artificial Intelligence in PDF only on Docsity!
Previous Page Next Page
Artificial Intelligence - Quick Guide Artificial Intelligence - Quick GuideArtificial Intelligence - Quick Guide
Advertisements
Artificial Intelligence - Overview Artificial Intelligence - OverviewArtificial Intelligence - Overview
Since the invention of computers or machines, their capability to perform various tasks went on growing exponentially. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time. A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings.
According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.
While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, “Can a machine think and behave like humans do?” Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.
To Create Expert Systems − The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.
What is Artificial Intelligence? What is Artificial Intelligence?What is Artificial Intelligence?
Philosophy of AI Philosophy of AIPhilosophy of AI
Goals of AI Goals of AIGoals of AI
To Implement Human Intelligence in Machines − Creating systems that understand, think, learn, and behave like humans.
Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving. Out of the following areas, one or multiple areas can contribute to build an intelligent system.
The programming without and with AI is different in following ways −
Programming Without AI Programming With AI
A computer program without AI can answer the specific questions it is meant to solve.
A computer program with AI can answer the generic questions it is meant to solve. Modification in the program leads to change in its structure.
AI programs can absorb new modifications by putting highly independent pieces of
What Contributes to AI? What Contributes to AI?What Contributes to AI?
Programming Without and With AI Programming Without and With AIProgramming Without and With AI
Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist. Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc. Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text. Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.
Here is the history of AI during 20th century −
Year Milestone / Innovation
Karel Čapek play named “Rossum's Universal Robots” (RUR) opens in London, first use of the word "robot" in English.
1943 Foundations for neural networks laid.
1945 Isaac Asimov, a Columbia University alumni, coined the term^ Robotics.
Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence. Claude Shannon published Detailed Analysis of Chess Playing as a search.
John McCarthy coined the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University.
1958 John McCarthy invents LISP programming language for AI.
Danny Bobrow's dissertation at MIT showed that computers can understand natural language well enough to solve algebra word problems correctly.
(^1965) Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on
History of AI History of AIHistory of AI
a dialogue in English.
Scientists at Stanford Research Institute Developed Shakey, a robot, equipped with locomotion, perception, and problem solving.
The Assembly Robotics group at Edinburgh University built Freddy, the Famous Scottish Robot, capable of using vision to locate and assemble models.
1979 The first computer-controlled autonomous vehicle, Stanford Cart, was built.
1985 Harold Cohen created and demonstrated the drawing program,^ Aaron.
Major advances in all areas of AI −
Significant demonstrations in machine learning Case-based reasoning Multi-agent planning Scheduling Data mining, Web Crawler natural language understanding and translation Vision, Virtual Reality Games
The Deep Blue Chess Program beats the then world chess champion, Garry Kasparov.
Interactive robot pets become commercially available. MIT displays Kismet, a robot with a face that expresses emotions. The robot Nomad explores remote regions of Antarctica and locates meteorites.
Artificial Intelligence - Intelligent Systems Artificial Intelligence - Intelligent SystemsArtificial Intelligence - Intelligent Systems
While studying artificially intelligence, you need to know what intelligence is. This chapter covers Idea of intelligence, types, and components of intelligence.
The ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems,
What is Intelligence? What is Intelligence?What is Intelligence?
The intelligence is intangible. It is composed of −
Reasoning Learning Problem Solving Perception Linguistic Intelligence
Let us go through all the components briefly −
Reasoning − It is the set of processes that enables us to provide basis for judgement, making decisions, and prediction. There are broadly two types −
Inductive Reasoning Deductive Reasoning
It conducts specific observations to makes broad general statements.
It starts with a general statement and examines the possibilities to reach a specific, logical conclusion.
Even if all of the premises are true in a statement, inductive reasoning allows for the conclusion to be false.
If something is true of a class of things in general, it is also true for all members of that class.
Example − "Nita is a teacher. Nita is studious. Therefore, All teachers are studious."
Example − "All women of age above 60 years are grandmothers. Shalini is 65 years. Therefore, Shalini is a grandmother."
Learning − It is the activity of gaining knowledge or skill by studying, practising, being taught, or experiencing something. Learning enhances the awareness of the subjects of the study. The ability of learning is possessed by humans, some animals, and AI-enabled systems. Learning is categorized as − Auditory Learning − It is learning by listening and hearing. For example, students listening to recorded audio lectures. Episodic Learning − To learn by remembering sequences of events that one has witnessed or experienced. This is linear and orderly.
Motor Learning − It is learning by precise movement of muscles. For example, picking objects, Writing, etc. Observational Learning − To learn by watching and imitating others. For example, child tries to learn by mimicking her parent. Perceptual Learning − It is learning to recognize stimuli that one has seen before. For example, identifying and classifying objects and situations. Relational Learning − It involves learning to differentiate among various stimuli on the basis of relational properties, rather than absolute properties. For Example, Adding ‘little less’ salt at the time of cooking potatoes that came up salty last time, when cooked with adding say a tablespoon of salt. Spatial Learning − It is learning through visual stimuli such as images, colors, maps, etc. For Example, A person can create roadmap in mind before actually following the road. Stimulus-Response Learning − It is learning to perform a particular behavior when a certain stimulus is present. For example, a dog raises its ear on hearing doorbell. Problem Solving − It is the process in which one perceives and tries to arrive at a desired solution from a present situation by taking some path, which is blocked by known or unknown hurdles. Problem solving also includes decision making, which is the process of selecting the best suitable alternative out of multiple alternatives to reach the desired goal are available. Perception − It is the process of acquiring, interpreting, selecting, and organizing sensory information. Perception presumes sensing. In humans, perception is aided by sensory organs. In the domain of AI, perception mechanism puts the data acquired by the sensors together in a meaningful manner. Linguistic Intelligence − It is one’s ability to use, comprehend, speak, and write the verbal and written language. It is important in interpersonal communication.
Humans perceive by patterns whereas the machines perceive by set of rules and data. Humans store and recall information by patterns, machines do it by searching algorithms. For example, the number 40404040 is easy to remember, store,
Difference between Human and Machine Intelligence Difference between Human and Machine IntelligenceDifference between Human and Machine Intelligence
menu navigation. its tone, voice pitch, and accent, etc.
Machine does not need training for Speech Recognition as it is not speaker dependent.
This recognition system needs training as it is person oriented.
Speaker independent Speech Recognition systems are difficult to develop.
Speaker dependent Speech Recognition systems are comparatively easy to develop.
The user input spoken at a microphone goes to sound card of the system. The converter turns the analog signal into equivalent digital signal for the speech processing. The database is used to compare the sound patterns to recognize the words. Finally, a reverse feedback is given to the database. This source-language text becomes input to the Translation Engine, which converts it to the target language text. They are supported with interactive GUI, large database of vocabulary, etc.
There is a large array of applications where AI is serving common people in their day- to-day lives −
Sr.No. Research Areas Real Life Application
(^1) Expert Systems Examples − Flight-tracking systems, Clinical systems.
(^2) Natural Language Processing Examples: Google Now feature, speech recognition, Automatic voice output.
(^3) Neural Networks Examples − Pattern recognition systems such as face recognition, character recognition, handwriting recognition.
(^4) Robotics Examples − Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, carving, etc.
(^5) Fuzzy Logic Systems
Working of Speech and Voice Recognition Systems Working of Speech and Voice Recognition SystemsWorking of Speech and Voice Recognition Systems
Real Life Applications of Research Areas Real Life Applications of Research AreasReal Life Applications of Research Areas
Examples − Consumer electronics, automobiles, etc.
The domain of AI is classified into Formal tasks, Mundane tasks, and Expert tasks.
Task Domains of Artificial Intelligence Mundane (Ordinary) Tasks Formal Tasks Expert Tasks
Perception Computer Vision Speech, Voice
Mathematics Geometry Logic Integration and Differentiation
Engineering Fault Finding Manufacturing Monitoring
Natural Language Processing Understanding Language Generation
Games Go Chess (Deep Blue)
Scientific Analysis
Task Classification of AI Task Classification of AITask Classification of AI
Performance Measure of Agent − It is the criteria, which determines how successful an agent is. Behavior of Agent − It is the action that agent performs after any given sequence of percepts. Percept − It is agent’s perceptual inputs at a given instance. Percept Sequence − It is the history of all that an agent has perceived till date. Agent Function − It is a map from the precept sequence to an action.
Rationality is nothing but status of being reasonable, sensible, and having good sense of judgment. Rationality is concerned with expected actions and results depending upon what the agent has perceived. Performing actions with the aim of obtaining useful information is an important part of rationality.
An ideal rational agent is the one, which is capable of doing expected actions to maximize its performance measure, on the basis of −
Its percept sequence Its built-in knowledge base
Rationality of an agent depends on the following −
The performance measures, which determine the degree of success. Agent’s Percept Sequence till now.
Agent Terminology Agent TerminologyAgent Terminology
Rationality RationalityRationality
What is Ideal Rational Agent? What is Ideal Rational Agent?What is Ideal Rational Agent?
The agent’s prior knowledge about the environment. The actions that the agent can carry out.
A rational agent always performs right action, where the right action means the action that causes the agent to be most successful in the given percept sequence. The problem the agent solves is characterized by Performance Measure, Environment, Actuators, and Sensors (PEAS).
Agent’s structure can be viewed as − Agent = Architecture + Agent Program Architecture = the machinery that an agent executes on. Agent Program = an implementation of an agent function.
They choose actions only based on the current percept. They are rational only if a correct decision is made only on the basis of current precept. Their environment is completely observable.
Condition-Action Rule − It is a rule that maps a state (condition) to an action.
They use a model of the world to choose their actions. They maintain an internal state. Model − knowledge about “how the things happen in the world”.
The Structure of Intelligent Agents The Structure of Intelligent AgentsThe Structure of Intelligent Agents
Simple Reflex Agents Simple Reflex AgentsSimple Reflex Agents
Model Based Reflex Agents Model Based Reflex AgentsModel Based Reflex Agents
There are conflicting goals, out of which only few can be achieved. Goals have some uncertainty of being achieved and you need to weigh likelihood of success against the importance of a goal.
Some programs operate in the entirely artificial environment confined to keyboard input, database, computer file systems and character output on a screen. In contrast, some software agents (software robots or softbots) exist in rich, unlimited softbots domains. The simulator has a very detailed, complex environment. The software agent needs to choose from a long array of actions in real time. A softbot designed to scan the online preferences of the customer and show interesting items to the customer works in the real as well as an artificial environment. The most famous artificial environment is the Turing Test environment, in which one real and other artificial agents are tested on equal ground. This is a very challenging environment as it is highly difficult for a software agent to perform as well as a human.
The success of an intelligent behavior of a system can be measured with Turing Test. Two persons and a machine to be evaluated participate in the test. Out of the two persons, one plays the role of the tester. Each of them sits in different rooms. The tester is unaware of who is machine and who is a human. He interrogates the questions by typing and sending them to both intelligences, to which he receives typed responses. This test aims at fooling the tester. If the tester fails to determine machine’s response from the human response, then the machine is said to be intelligent.
The Nature of Environments The Nature of EnvironmentsThe Nature of Environments
Turing Test Turing TestTuring Test
Properties of Environment Properties of EnvironmentProperties of Environment
The environment has multifold properties −
Discrete / Continuous − If there are a limited number of distinct, clearly defined, states of the environment, the environment is discrete (For example, chess); otherwise it is continuous (For example, driving). Observable / Partially Observable − If it is possible to determine the complete state of the environment at each time point from the percepts it is observable; otherwise it is only partially observable. Static / Dynamic − If the environment does not change while an agent is acting, then it is static; otherwise it is dynamic. Single agent / Multiple agents − The environment may contain other agents which may be of the same or different kind as that of the agent. Accessible / Inaccessible − If the agent’s sensory apparatus can have access to the complete state of the environment, then the environment is accessible to that agent. Deterministic / Non-deterministic − If the next state of the environment is completely determined by the current state and the actions of the agent, then the environment is deterministic; otherwise it is non-deterministic. Episodic / Non-episodic − In an episodic environment, each episode consists of the agent perceiving and then acting. The quality of its action depends just on the episode itself. Subsequent episodes do not depend on the actions in the previous episodes. Episodic environments are much simpler because the agent does not need to think ahead.
AI - Popular Search Algorithms AI - Popular Search AlgorithmsAI - Popular Search Algorithms
Searching is the universal technique of problem solving in AI. There are some single- player games such as tile games, Sudoku, crossword, etc. The search algorithms help you to search for a particular position in such games.
The games such as 3X3 eight-tile, 4X4 fifteen-tile, and 5X5 twenty four tile puzzles are single-agent-path-finding challenges. They consist of a matrix of tiles with a blank tile. The player is required to arrange the tiles by sliding a tile either vertically or horizontally into a blank space with the aim of accomplishing some objective. The other examples of single agent pathfinding problems are Travelling Salesman Problem, Rubik’s Cube, and Theorem Proving.
Single Agent Pathfinding Problems Single Agent Pathfinding ProblemsSingle Agent Pathfinding Problems
Search Terminology Search TerminologySearch Terminology
It is implemented in recursion with LIFO stack data structure. It creates the same set of nodes as Breadth-First method, only in the different order. As the nodes on the single path are stored in each iteration from root to leaf node, the space requirement to store nodes is linear. With branching factor b and depth as m, the storage space is bm. Disadvantage − This algorithm may not terminate and go on infinitely on one path. The solution to this issue is to choose a cut-off depth. If the ideal cut-off is d, and if chosen cut-off is lesser than d, then this algorithm may fail. If chosen cut-off is more than d, then execution time increases. Its complexity depends on the number of paths. It cannot check duplicate nodes.
It searches forward from initial state and backward from goal state till both meet to identify a common state. The path from initial state is concatenated with the inverse path from the goal state. Each search is done only up to half of the total path.
Sorting is done in increasing cost of the path to a node. It always expands the least cost node. It is identical to Breadth First search if each transition has the same cost. It explores paths in the increasing order of cost. Disadvantage − There can be multiple long paths with the cost ≤ C*. Uniform Cost search must explore them all.
Depth-First Search Depth-First SearchDepth-First Search
Bidirectional Search Bidirectional SearchBidirectional Search
Uniform Cost Search Uniform Cost SearchUniform Cost Search
It performs depth-first search to level 1, starts over, executes a complete depth-first search to level 2, and continues in such way till the solution is found. It never creates a node until all lower nodes are generated. It only saves a stack of nodes. The algorithm ends when it finds a solution at depth d. The number of nodes created at depth d is bd^ and at depth d-1 is bd-1.
Let us see the performance of algorithms based on various criteria −
Criterion BreadthFirst DepthFirst Bidirectional UniformCost InteractiveDeepening
Time (^) bd^ bm^ bd/2^ bd^ bd
Space (^) bd^ bm^ bd/2^ bd^ bd
Optimality Yes No Yes Yes Yes
Completeness Yes No Yes Yes Yes
To solve large problems with large number of possible states, problem-specific knowledge needs to be added to increase the efficiency of search algorithms.
They calculate the cost of optimal path between two states. A heuristic function for sliding-tiles games is computed by counting number of moves that each tile makes from its goal state and adding these number of moves for all tiles.
Iterative Deepening Depth-First Search Iterative Deepening Depth-First SearchIterative Deepening Depth-First Search
Comparison of Various Algorithms Complexities Comparison of Various Algorithms ComplexitiesComparison of Various Algorithms Complexities
Informed (Heuristic) Search Strategies Informed (Heuristic) Search StrategiesInformed (Heuristic) Search Strategies
Heuristic Evaluation Functions Heuristic Evaluation FunctionsHeuristic Evaluation Functions
Pure Heuristic Search Pure Heuristic SearchPure Heuristic Search
