Introduction to Artificial Intelligence - Unit I

 

1. Introduction to Artificial Intelligence

Artificial Intelligence and related fields, brief history of AI, applications of AI, Definition & importance of knowledge & learning, Agent & its type and performance measures.

Intelligence

Intelligence is more than mere knowledge. It represents many skills such as planning, prediction, evaluation, solving and learning. Other skills such as perception and communication can also be included.

Artificial Intelligence (AI)

In very simple terms, AI is the ability to build computer systems that are capable of working like a human intelligence such as visual perception, speech recognition, decision making and translation between languages.

Development of AI

1. Charles Babbage and the Analytical engine (1842)

2. Boole, An investigation into the Laws of Thought (1815-64)

3. Rullell and Wittgenstein, work on logic and language(1889-1951)

4. Turing Test (1951) - explained later

5. Claude Shannon, Programming a computer for playing chess (1950)

6. Evans and the ANALOGY program to solve geometrical IQ tests - eplained later

7. Symbolic integration programs leading to packages such as MACSYMA, MAPLE, SMP and MATHEMATICA

8. Roberts, WalZ's line-labelling to explain shape form imagery (1965)

9. Rule-based expert systems developed in 1970s

Behaviors of the AI

The behaviors of the AI are as follows:

Systems that think like humans	System that think rationally
The exciting new effort to make computers think like machines with mind, in the full and literal sense. activities that we associate with human thinking, activities such as decision making, problem solving, learning.	The study of mental faculties through the use of computational models. The study of the computations that make it possible to perceive, reason, and act.
Systems that act like humans	System that act rationally
The art of creating machines that perform functions that require intelligence when performed by people. The study of how to make computers do things at which, at the moment, people are better.	Computational Intelligence is the study of the design of intelligent agents. AI  is concerned with intelligent behavior in artifacts.

The Turing Test:

The Turing test, proposed by Alan Turing (1950) was designed to convince the people that whether a particular machine can think or not. He suggested a test based on indistinguishability from undeniably intelligent entities-human beings. The test involves an interrogator who interacts with one human and one machine. Within a given time the interrogator has to find out which of the two the human is, and which one the machine.

The computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written response came from the human or not. 

To pass a Turing test, a computer must have following capabilities:

• Natural Language Processing: Must be able to communicate successfully in English

• Knowledge representation: To store what it knows and hears.

• Automated reasoning: Answer the Questions based on the stored information.

• Machine learning: Must be able to adapt in new circumstances.

Turing tests avoid the physical interaction with human interrogators. Physical simulation of human beings is not necessary for testing intelligence.

Application of AI

• Game Playing

• Speech recognition

• Understanding natural language

• Computer vision- 2D, 3D

• Expert System =>Diagnosis of Bacterial infection and suggest treatment

• Heuristic classification => Credit card purchase and accept

Knowledge:

Knowledge is the ability to convert data and information in effective actions.

Learning:

It is concerned with designs and development of algorithms that allow computers to evolve behaviors based on empirical data such as from sensor data.A major focus of learning is to automatically learn to recognize complex patterns and make intelligent decisions based on data.

Agent:

An agent is anything that perceives the environment and takes action on the environment.

If the action of an agent is right/rational on the basis of given information, then it is known as rational agent.

Agent → Percept → Decision → Action

Goals of Agent:

• High performance

• Optimized result

• Rational Action (right action)

PEAS based grouping of Agents

PEAS stands for Performance, Environment, Actuators, and Sensors.

Performance:

The output which we get from the agent. All the necessary results that an agent gives after processing comes under its performance.

Environment:

All the surrounding things and conditions of an agent fall in this section. It basically consists of all the things under which the agents work.

Actuators:

The devices, hardware or software through which the agent performs any actions or processes any information to produce a result are the actuators of the agent.

Sensors:

The devices through which the agent observes and perceives its environment are the sensors of the agent.

EXAMPLE:

1. Consider the task of designing an automated Car

Performance measure: Safe, fast, comfortable trip, maximize profits

Environment: Roads, other traffic, pedestrians

Actuators: Steering wheel, accelerator, brake, signal, horn

Sensors: Cameras, speedometer, GPS, odometer, engine sensors

2. Medical diagnosis system

Performance measure: Healthy patient, minimize costs, lawsuits

Environment: Patient, hospital, staff

Actuators: Screen display (Questions, tests, diagnoses, treatments, referrals)

Sensors: Keyboard (entry of symptoms, findings, patient's answers)

3. Part picking robot

Performance measure: Percentage of parts in correct bins

Environment: Conveyor belt with parts, bins

Actuators: Jointed arm and hand

Sensors: Camera, joint angle sensors

4. Satellite image analysis system

Performance measure: Correct categorizations

Environment: Downlink from orbiting satellite

Actuators: Display categorization of scene

Sensors: Colour pixel arrays

5. Refinery controller

Performance measure: maximize purity, yield, safety

Environment: Refinery, operator

Actuators: Valves, pumps, heater, displays

Sensors: Temperature, pressure and chemical sensors

Types of Agent:

Based on their degree of perceived intelligence and capability. Agents are of following types:

1. Table-driven agents

use a percept sequence/action table in memory to find the next action. They are implemented by a (large) lookup table.

2. Simple reflex agents

• This agent works only on the basis of current perception and it does not bother about the history or previous state in which the system was.

• This type of agent is based upon the condition-action rule. If the condition is true, then the action is taken, else not.

• PROBLEMS FACED:

1. Very limited intelligence.

2. No knowledge about the non-perceptual parts of the state.

3. Operating in a partially observable environment, infinite loops are unavoidable.

3. Model based reflex agents

• It works by finding a rule whose condition matches the current situation.

• It can handle partially observable environments.

• Updating the state requires information about how the world evolves independently from the agent and how the agent actions affect the world.

4. Goal based reflex agents

• The goal based agent focuses only on reaching the goal set and hence the decision took by the agent is based on how far it is currently from their goal or desired state.

• Their every action is intended to minimize their distance from the goal.

• This agent is more flexible, and the agent develops its decision making skill by choosing the right from the various options available.

5. Utility based agents

• These agents are more concerned about the preference(utility) for each state. When there are multiple options available, the utility based agent takes the decision on the basis of how much satisfaction the agent gets from it.

• This approach was like somewhat adding emotions to the agent, because, after taking any decision, the agent ensures that "how happy I Am after taking this decision?".

• This agent was developed because sometimes achieving the desired goal is not enough. We may look for quicker, safer and cheaper alternate to reach the destination.

Simple reflex agents Model based reflex agents

Goal based reflex agents Utility based agents