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

2. Outline
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— Agents and environments
— Rationality
— PEAS (Performance measure, Environment,
Actuators, Sensors)

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

5. Agent Examples
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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

7. 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

8. Agent and Environment
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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.)

10. Vacuum-cleaner world
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— 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

11. Vacuum-cleaner world
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12. Agent function – Lookup table
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13. Rational Agent
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14. Rational agents
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• 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.

15. Back to vacuum cleaner agent
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16. Back to vacuum cleaner agent
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17. Vacuum cleaner agent - irrational
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18. Rationality
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— 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.

19. 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

20. 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.

21. Specifying the task environment (PEAS)
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22. Specifying the task environment (PEAS)
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23. PEAS – vacuum cleaner
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24. PEAS – Windshield Wiper Agent

25. 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 …

26. PEAS – self driving car
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27. PEAS - automated taxi driver
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28. PEAS - automated taxi driver
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• 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

29. 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

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

31. PEAS – medical diagnosis system
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32. PEAS – medical diagnosis system
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33. PEAS – spam filter
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34. PEAS – spam filter
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35. PEAS – satellite image analysis system
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36. PEAS – satellite image analysis system
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37. PEAS - Part-picking robot
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38. PEAS - Part-picking robot
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— Performance measure: Percentage of parts in correct bins
— Environment: Conveyor belt with parts, bins
— Actuators: Jointed arm and hand
— Sensors: Camera, joint angle sensors

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

41. Questions
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