LectureNote2.pdf

Mansoura University
Faculty of Computers and Information
Course Name: Artificial Intelligence
Lecturer: Amir EL-Ghamry
Topic: Intelligent Agents

Outline
Artificial Intelligence a modern approach
2
— Agents and environments
— Rationality
— PEAS (Performance measure, Environment,
Actuators, Sensors)

Agent
3

CS 561, Lecture 2
Agent
Environment
Agent
percepts
actions
?
Sensors
Actuators
How to design this?

Agent Examples
6

CS 561, Lecture 2
How is an Agent different from other
software?
— Agents are autonomous, that is, they act on behalf of
the user
— Agents contain some level of intelligence, from fixed
rules to learning engines that allow them to adapt to
changes in the environment
— Agents have social ability, that is, they communicate
with the user, the system, and other agents as required
— Agents may also cooperate with other agents to carry
out more complex tasks than they themselves can handle

CS 561, Lecture 2
How is an Agent different from other
software?
— Agents may migrate from one system to another to
access remote resources or even to meet other agents

Agent and Environment
9

CS 561, Lecture 2
Structure of Intelligent Agents
— Agent program: the implementation of f : P* ® A, the
agent’s perception-action mapping
function Skeleton-Agent(Percept) returns Action
memory ¬ UpdateMemory(memory, Percept)
Action ¬ ChooseBestAction(memory)
memory ¬ UpdateMemory(memory, Action)
return Action
— Architecture: a device that can execute the agent
program (e.g., general-purpose computer, specialized
device, etc.)

Vacuum-cleaner world
12
— Percepts: location (A or B) and contents (dirt or not),
e.g., [A,Dirty]
— Actions: Left, Right, Suck, NoOp
— Agent’s function à look-up table
¡ For many agents this is a very large table

Vacuum-cleaner world
13

Agent function – Lookup table
14

Rational Agent
15

Rational agents
16
• Rationality – Good behavior
1. Performance measuring success
2. Agents prior knowledge of environment
3. Actions that agent can perform
4. Agent’s percept sequence to date
• Rational Agent: For each possible percept sequence, a
rational agent should select an action that is expected to
maximize its performance measure, given the evidence
provided by the percept sequence and whatever built-in
knowledge the agent has.

Back to vacuum cleaner agent
17

Back to vacuum cleaner agent
18

Vacuum cleaner agent - irrational
19

Rationality
21
— Rational is different from omniscience (all knowing
with infinite knowledge)
¡ Percepts may not supply all relevant information
¡ E.g., in card game, don’t know cards of others.
— Rational is different from being perfect
¡ Rationality maximizes expected outcome
¡ Perfection (omniscience) maximizes actual outcome.

The Right Thing = The Rational Action
— Rational Action: The action that maximizes the expected
value of the performance measure given the percept sequence
to date
¡ Rational = Best Yes, to the best of its knowledge
¡ Rational = Optimal Yes, to the best of its abilities (constraints).
¡ Rational ¹ Omniscience
¡ Rational ¹ Successful

Autonomy in Agents
— Extremes
¡ No autonomy – ignores environment/data
¡ Complete autonomy – must act randomly/no program
— Example: baby learning to crawl
— Ideal: design agents to have some autonomy
¡ Possibly become more autonomous with experience
The autonomy of an agent is the extent to which its
behaviour is determined by its own experience,
rather than knowledge of designer.

Specifying the task environment (PEAS)
24

Specifying the task environment (PEAS)
25

PEAS – vacuum cleaner
26

PEAS – Windshield Wiper Agent

PEAS – Windshield Wiper Agent
— Goals: Keep windshields clean & maintain visibility
— Percepts: Raining, Dirty
— Sensors: Camera (moist sensor)
— Actuators: Wipers (left, right, back)
— Actions: Off, Slow, Medium, Fast
— Environment: Inner city, freeways, highways, weather …

PEAS – self driving car
29

PEAS - automated taxi driver
30

PEAS - automated taxi driver
31
• The task of designing an automated taxi driver:
– Performance measure: Safe, fast, legal, comfortable
trip, maximize profits
– Environment: Roads, other traffic, pedestrians,
customers , weather
– Actuators: Steering wheel, accelerator, brake, signal,
horn
– Sensors: Cameras, sonar, speedometer, GPS, odometer,
engine sensors, keyboard

Interacting Agents
Collision Avoidance Agent (CAA)
— Goals: Avoid running into obstacles
— Percepts: Obstacle distance, velocity, trajectory
— Sensors: Vision, proximity sensing
— Actuators: Steering Wheel, Accelerator, Brakes, Horn, Headlights
— Actions: Steer, speed up, brake, blow horn, signal (headlights)
— Environment: Freeway
Lane Keeping Agent (LKA)
• Goals: Stay in current lane
• Percepts: Lane center, lane boundaries
• Sensors: Vision
• Effectors: Steering Wheel, Accelerator, Brakes
• Actions: Steer, speed up, brake
• Environment: Freeway

Conflict Resolution by Action Selection Agents
• Arbitrate: if Obstacle is Close then CAA
else LKA
• Challenges: Doing the right thing

PEAS – medical diagnosis system
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PEAS – medical diagnosis system
35

PEAS – spam filter
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PEAS – spam filter
37

PEAS – satellite image analysis system
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PEAS – satellite image analysis system
39

PEAS - Part-picking robot
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PEAS - Part-picking robot
41
— Performance measure: Percentage of parts in correct bins
— Environment: Conveyor belt with parts, bins
— Actuators: Jointed arm and hand
— Sensors: Camera, joint angle sensors

PEAS - Interactive English tutor
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PEAS - Interactive English tutor
43
— Performance measure: Maximize student's score on
test
— Environment: Set of students
— Actuators: Screen display (exercises, suggestions,
corrections)
— Sensors: Keyboard

Questions
44

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