To Create Expert Systems − The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.

To Implement Human Intelligence in Machines − Creating systems that understand, think, learn, and behave like humans.

Its volume is huge, next to unimaginable.

It is not well-organized or well-formatted.

It keeps changing constantly.

It should be perceivable by the people who provide it.

It should be easily modifiable to correct errors.

It should be useful in many situations though it is incomplete or inaccurate.

Gaming − AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

Natural Language Processing − It is possible to interact with the computer that understands natural language spoken by humans.

Expert Systems − There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.

Vision Systems − These systems understand, interpret, and comprehend visual input on the computer. For example, A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas. Doctors use clinical expert system to diagnose the patient. 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.

Reasoning

Learning

Problem Solving

Perception

Linguistic Intelligence

Reasoning − It is the set of processes that enables us to provide basis for judgement, making decisions, and prediction. There are broadly two types −

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, and recall as its pattern is simple.

Humans can figure out the complete object even if some part of it is missing or distorted; whereas the machines cannot do it correctly.

A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel to the sensors, and other organs such as hands, legs, mouth, for effectors.

A robotic agent replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors.

A software agent has encoded bit strings as its programs and actions.

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.

Its percept sequence

Its built-in knowledge base

The performance measures, which determine the degree of success.

Agent’s Percept Sequence till now.

The agent’s prior knowledge about the environment.

The actions that the agent can carry out.

Agent = Architecture + Agent Program

Architecture = the machinery that an agent executes on.

Agent Program = an implementation of an agent function.

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.

Problem Space − It is the environment in which the search takes place. (A set of states and set of operators to change those states)

Problem Instance − It is Initial state + Goal state.

Problem Space Graph − It represents problem state. States are shown by nodes and operators are shown by edges.

Depth of a problem − Length of a shortest path or shortest sequence of operators from Initial State to goal state.

Space Complexity − The maximum number of nodes that are stored in memory.

Time Complexity − The maximum number of nodes that are created.

Admissibility − A property of an algorithm to always find an optimal solution.

Branching Factor − The average number of child nodes in the problem space graph.

Depth − Length of the shortest path from initial state to goal state.

State description

A set of valid operators

Initial state

Goal state description

It can control machines and consumer products.

It may not give accurate reasoning, but acceptable reasoning.

Fuzzy logic helps to deal with the uncertainty in engineering.

Fuzzification Module − It transforms the system inputs, which are crisp numbers, into fuzzy sets. It splits the input signal into five steps such as −

Knowledge Base − It stores IF-THEN rules provided by experts.

Inference Engine − It simulates the human reasoning process by making fuzzy inference on the inputs and IF-THEN rules.

Defuzzification Module − It transforms the fuzzy set obtained by the inference engine into a crisp value.

Automatic Gearboxes

Four-Wheel Steering

Vehicle environment control

Hi-Fi Systems

Photocopiers

Still and Video Cameras

Television

Microwave Ovens

Refrigerators

Toasters

Vacuum Cleaners

Washing Machines

Air Conditioners/Dryers/Heaters

Humidifiers

Mathematical concepts within fuzzy reasoning are very simple.

You can modify a FLS by just adding or deleting rules due to flexibility of fuzzy logic.

Fuzzy logic Systems can take imprecise, distorted, noisy input information.

FLSs are easy to construct and understand.

Fuzzy logic is a solution to complex problems in all fields of life, including medicine, as it resembles human reasoning and decision making.

There is no systematic approach to fuzzy system designing.

They are understandable only when simple.

They are suitable for the problems which do not need high accuracy.

Lexical ambiguity − It is at very primitive level such as word-level.

For example, treating the word “board” as noun or verb?

Syntax Level ambiguity − A sentence can be parsed in different ways.

For example, “He lifted the beetle with red cap.” − Did he use cap to lift the beetle or he lifted a beetle that had red cap?

Referential ambiguity − Referring to something using pronouns. For example, Rima went to Gauri. She said, “I am tired.” − Exactly who is tired?

One input can mean different meanings.

Many inputs can mean the same thing.

Phonology − It is study of organizing sound systematically.

Morphology − It is a study of construction of words from primitive meaningful units.

Morpheme − It is primitive unit of meaning in a language.

Syntax − It refers to arranging words to make a sentence. It also involves determining the structural role of words in the sentence and in phrases.

Semantics − It is concerned with the meaning of words and how to combine words into meaningful phrases and sentences.

Pragmatics − It deals with using and understanding sentences in different situations and how the interpretation of the sentence is affected.

Discourse − It deals with how the immediately preceding sentence can affect the interpretation of the next sentence.

World Knowledge − It includes the general knowledge about the world.

Lexical Analysis − It involves identifying and analyzing the structure of words. Lexicon of a language means the collection of words and phrases in a language. Lexical analysis is dividing the whole chunk of txt into paragraphs, sentences, and words.

Syntactic Analysis (Parsing) − It involves analysis of words in the sentence for grammar and arranging words in a manner that shows the relationship among the words. The sentence such as “The school goes to boy” is rejected by English syntactic analyzer.

Semantic Analysis − It draws the exact meaning or the dictionary meaning from the text. The text is checked for meaningfulness. It is done by mapping syntactic structures and objects in the task domain. The semantic analyzer disregards sentence such as “hot ice-cream”.

Discourse Integration − The meaning of any sentence depends upon the meaning of the sentence just before it. In addition, it also brings about the meaning of immediately succeeding sentence.

Pragmatic Analysis − During this, what was said is re-interpreted on what it actually meant. It involves deriving those aspects of language which require real world knowledge.

Context-Free Grammar

Top-Down Parser

Advising

Instructing and assisting human in decision making

Demonstrating

Deriving a solution

Diagnosing

Explaining

Interpreting input

Predicting results

Justifying the conclusion

Suggesting alternative options to a problem

Substituting human decision makers

Possessing human capabilities

Producing accurate output for inadequate knowledge base

Refining their own knowledge

Knowledge Base

Inference Engine

User Interface

Applies rules repeatedly to the facts, which are obtained from earlier rule application.

Adds new knowledge into the knowledge base if required.

Resolves rules conflict when multiple rules are applicable to a particular case.

Forward Chaining

Backward Chaining

Natural language displayed on screen.

Verbal narrations in natural language.

Listing of rule numbers displayed on the screen.

Limitations of the technology

Difficult knowledge acquisition

ES are difficult to maintain

High development costs

Expert System Development Environment − The ES development environment includes hardware and tools. They are − Workstations, minicomputers, mainframes. High level Symbolic Programming Languages such as *LIS*t *P*rogramming (LISP) and *PRO*grammation en *LOG*ique (PROLOG). Large databases.

Tools − They reduce the effort and cost involved in developing an expert system to large extent. Powerful editors and debugging tools with multi-windows. They provide rapid prototyping Have Inbuilt definitions of model, knowledge representation, and inference design.

Shells − A shell is nothing but an expert system without knowledge base. A shell provides the developers with knowledge acquisition, inference engine, user interface, and explanation facility. For example, few shells are given below − Java Expert System Shell (JESS) that provides fully developed Java API for creating an expert system. Vidwan, a shell developed at the National Centre for Software Technology, Mumbai in 1993. It enables knowledge encoding in the form of IF-THEN rules.

Availability − They are easily available due to mass production of software.

Less Production Cost − Production cost is reasonable. This makes them affordable.

Speed − They offer great speed. They reduce the amount of work an individual puts in.

Less Error Rate − Error rate is low as compared to human errors.

Reducing Risk − They can work in the environment dangerous to humans.

Steady response − They work steadily without getting motional, tensed or fatigued.

Legged

Wheeled

Combination of Legged and Wheeled Locomotion

Tracked slip/skid

Power Supply − The robots are powered by batteries, solar power, hydraulic, or pneumatic power sources.

Actuators − They convert energy into movement.

Electric motors (AC/DC) − They are required for rotational movement.

Pneumatic Air Muscles − They contract almost 40% when air is sucked in them.

Muscle Wires − They contract by 5% when electric current is passed through them.

Piezo Motors and Ultrasonic Motors − Best for industrial robots.

Sensors − They provide knowledge of real time information on the task environment. Robots are equipped with vision sensors to be to compute the depth in the environment. A tactile sensor imitates the mechanical properties of touch receptors of human fingertips.

OCR − In the domain of computers, Optical Character Reader, a software to convert scanned documents into editable text, which accompanies a scanner.

Face Detection − Many state-of-the-art cameras come with this feature, which enables to read the face and take the picture of that perfect expression. It is used to let a user access the software on correct match.

Object Recognition − They are installed in supermarkets, cameras, high-end cars such as BMW, GM, and Volvo.

Estimating Position − It is estimating position of an object with respect to camera as in position of tumor in human’s body.

Agriculture

Autonomous vehicles

Biometrics

Character recognition

Forensics, security, and surveillance

Industrial quality inspection

Face recognition

Gesture analysis

Geoscience

Medical imagery

Pollution monitoring

Process control

Remote sensing

Robotics

Transport

Industries − Robots are used for handling material, cutting, welding, color coating, drilling, polishing, etc.

Military − Autonomous robots can reach inaccessible and hazardous zones during war. A robot named Daksh, developed by Defense Research and Development Organization (DRDO), is in function to destroy life-threatening objects safely.

Medicine − The robots are capable of carrying out hundreds of clinical tests simultaneously, rehabilitating permanently disabled people, and performing complex surgeries such as brain tumors.

Exploration − The robot rock climbers used for space exploration, underwater drones used for ocean exploration are to name a few.

Entertainment − Disney’s engineers have created hundreds of robots for movie making.

Supervised Learning − It involves a teacher that is scholar than the ANN itself. For example, the teacher feeds some example data about which the teacher already knows the answers. For example, pattern recognizing. The ANN comes up with guesses while recognizing. Then the teacher provides the ANN with the answers. The network then compares it guesses with the teacher’s “correct” answers and makes adjustments according to errors.

Unsupervised Learning − It is required when there is no example data set with known answers. For example, searching for a hidden pattern. In this case, clustering i.e. dividing a set of elements into groups according to some unknown pattern is carried out based on the existing data sets present.

Reinforcement Learning − This strategy built on observation. The ANN makes a decision by observing its environment. If the observation is negative, the network adjusts its weights to be able to make a different required decision the next time.

Range of prepositions

Probability assigned to each of the prepositions.

Aerospace − Autopilot aircrafts, aircraft fault detection.

Automotive − Automobile guidance systems.

Military − Weapon orientation and steering, target tracking, object discrimination, facial recognition, signal/image identification.

Electronics − Code sequence prediction, IC chip layout, chip failure analysis, machine vision, voice synthesis.

Financial − Real estate appraisal, loan advisor, mortgage screening, corporate bond rating, portfolio trading program, corporate financial analysis, currency value prediction, document readers, credit application evaluators.

Industrial − Manufacturing process control, product design and analysis, quality inspection systems, welding quality analysis, paper quality prediction, chemical product design analysis, dynamic modeling of chemical process systems, machine maintenance analysis, project bidding, planning, and management.

Medical − Cancer cell analysis, EEG and ECG analysis, prosthetic design, transplant time optimizer.

Speech − Speech recognition, speech classification, text to speech conversion.

Telecommunications − Image and data compression, automated information services, real-time spoken language translation.

Transportation − Truck Brake system diagnosis, vehicle scheduling, routing systems.

Software − Pattern Recognition in facial recognition, optical character recognition, etc.

Time Series Prediction − ANNs are used to make predictions on stocks and natural calamities.

Signal Processing − Neural networks can be trained to process an audio signal and filter it appropriately in the hearing aids.

Control − ANNs are often used to make steering decisions of physical vehicles.

Anomaly Detection − As ANNs are expert at recognizing patterns, they can also be trained to generate an output when something unusual occurs that misfits the pattern.