ARTIFICIAL INTELLIGENCE. LECTURE-3. L ets remember what do you mean by Artificial intelligence and the application of artificial intelligence. OBJECTIVE OF TODAY’S LECTURE.
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Lets remember what do you mean by Artificial intelligence and the application of artificial intelligence
OBJECTIVE OF TODAY’S LECTURE
Today we are going to study about details of Intelligent Agents. In which we discuss what an intelligent agent does , how it is related to its environment, how it is evaluated , and how we might go about building one.
Our aim in this lecture is to design agents that do a good job of acting on their environment.
THE MAINE POINTS OF TODAYS LECTURE
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.
# A human agent has eyes,ears, and other organs for sensors and hands,legs,mouth, and other body parts for effectors.
# A robotic agent substitutes cameras and infrared range finders for the sensors and various motors for the effectors.
# A software agent has encoded bit strings as its percepts and actions
A generic agent is diagrammed in next slide
Figure:3.1:-Agents interact with environment through sensors and effectors
A rational agent is one that does the right thing. The right action is one that will cause the agent to be most successful.
We use the term “performance measure” to determine how successful an agent is.
Example of performance measure:
Consider the case of an agent that is supposed to vacuum a dirty floor. The performance measure would be the amount of dirt cleaned up in a single eight-hour shift. performance is measure by two factors 1.electricity measure 2.noise generation.
A third performance measure might give highest marks to an agent who not only cleans the floor quietly and efficiently but also finds time to go windsurfing at the weekend.
We should measure performance over long run, be it eight-hours shift or a life time.
We need to be careful to distinguish between rationality and omniscience. An omniscient agent knows the actual outcome of its actions and can act accordingly; but omniscience is impossible in reality.
Ideal rational agent:
For each possible percept sequence, an ideal rational agent should do whatever action is expected to maximize its performance measure, on the basis of the evidence provide by the percept sequence and whatever built-in knowledge the agent has.
The ideal mapping from percept sequences to action:
A mapping from percept sequence to actions is a list or a table that represents an agents behavior of taking action in response to each possible percept sequence.
The ideal mapping specify which action an agent ought to take in response to any given percept sequence provides a design for an ideal length.
consider a very simple agent : the square-root function on a calculator. The percept sequence for this agent is a sequence of keystrokes representing a number and the action is to display a number on the display screen.The ideal mapping is that when the percept is a positive number x,the right action is to display a positive number z .
The job of AI is to design the agent program.Agent program is a function that implements the agent mapping from percepts to action.
We assume this program will run on some sort of computing device which we will call; the architecture.
The relationship among agents,architecture and programs can be summed up as follows:
Agent = Architecture + program.
Before we design an agent program, we must have a pretty idea of the possible percepts and action.
--What goals or performance the agent is supposed to achieve.
--What sort of environment it will operate in.
Robot agent works generally in small environment or in limited domain.
A robot design to inspects parts as they come by on a conveyer belt; the only thing on the conveyer belt will be parts of a certain kind and there are only two actions-accept the part or mark it as a reject.
In contrast, some software agents (or software robots or softbots ) exist in each unlimited domains.
A “softbot” designed to scan online news sources and show the interesting items to its customers. To do well, it will need some natural processing abilities, it will need to learn what each customer is interested in and it will need to dynamically change its plans when,
for example the connection for one news source crashes or a new one comes on line.
Examples of agent types and their PAGE descriptions:
Why not just look up the answers?
1. The table needed for something as an agent that can only play chess would be about 35^100 entries.
2. It would take quite a long time for the designer to build the table.
3. The agent has no autonomy at all,because the calculation of best actions is entirely built-in.so if the environment changed in some unexpected way,the agent would be lost.
4.Even if we gave the agent a learning mechanism as well, so that it could have a degree of autonomy, it would take forever to learn the right value for all the table entries.
-percepts provided by one or more controllable TV cameras,the speedometer,odometer.
-To control the vehicle, it should have an accelerometer.
- To know the mechanical state of the vehicle,so it will need array of engine and electrical system sensors.
-need a satellite global positioning system(GPS) to give it accurate position information with respect to an electronic map.
-infrared or sonar sensors to detect distances to other cars and obstacles.
-It will need a microphone or keyboard for the passengers to tell it their destination.
-control over the engine through the gas pedal and control over steering and braking.
-to talk back to the passengers and to communicate with other vehicles.