1. Artificial Intelligence
2. Intelligent Agents
Dr. M. Tounsi

2. Definition Is anything: Example: With Robotic agent
perceiving its environment through sensors
acting upon its environment through effectors
Example: With Robotic agent
Sensors: Cameras and infrared
Effectors: various motors

4. Definitions and Concepts
Rational Agent
One that does the right thing (most successful!)
Issue: how and when to evaluate the agent’s success ?
Performance Measure
Criteria that determines how successful an agent is
Percept Sequence
Everything that the agent has perceived so far
Ideal Rational Agent
Should do whatever action is expected to maximize its performance measure based on percept sequence and whatever build-in knowledge the agent has

5. Rational agents
An agent should strive to "do the right thing", based on what it can perceive and the actions it can perform. The right action is the one that will cause the agent to be most successful
Performance measure: An objective criterion for success of an agent's behavior
E.g., performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.

6. PEAS PEAS: Performance measure, Environment, Actuators, Sensors
Definition: Actuator = Effector
Must first specify the setting for intelligent agent design
Example: the task of designing an automated taxi driver:
Performance measure: Safe, fast, legal, comfortable trip, maximize profits
Environment: Roads, other traffic, pedestrians, customers
Actuators: Steering wheel, accelerator, brake, signal, horn
Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard

7. PEAS Agent: Medical diagnosis system
Performance measure: Healthy patient, minimize costs,…
Environment: Patient, hospital, staff
Actuators: Screen display (questions, tests, diagnoses, treatments, referrals)
Sensors: Keyboard (entry of symptoms, findings, patient's answers)

8. PEAS Agent: 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

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

10. Agent functions and programs
An agent is represented by the agent function which maps percept sequences to actions
Aim: find a way to implement the rational agent function concisely

11. Table-lookup agent Function Table-Agent(percept) returns an Action
Static: percepts // sequence, initially empty
Table // table of actions, indexed by percept, initially fully specified
Begin
Append perept to the end of the percepts
Action := LOOKUP(percepts, Table)
return Action
End
Drawbacks:
Huge table
Take a long time to build the table
No autonomy
Even with learning, need a long time to learn the table entries

12. Program Agent
The design of the agent depends on the percepts, actions, goals and environment
Function SKELETON-AGENT (percept)
Static: memory;
memory <- Update-Mem (memory, percept)
action <- Choose-Best-Action (Memory)
Memory <- Update-Memory(memory, action)
Return action

13. Agent types Four basic types in order of increasing generality:
Simple reflex agents
respond immediately to percepts (i.e.: rules)
Model-based reflex agents
Goal-based agents
act so that they will achieve their goal(s)
Utility-based agents
try to maximize their own happiness

14. Example: Simplex reflex agent
Function Reflex-Vacum_agent(location, status) return actions {
If status = dirty then action= Clean_ON;
else {
if location = A then action=right;
else action = left; }

15. AI Agent should have initial knowledge as well as an ability to learn
Autonomy
If the agent is based completely on the “built-in knowledge” part, such that it pays no attention to its percepts, then we say that the agent lacks autonomy
If the behavior of the agent is determined by only its own experience  the system is autonomous
AI Agent should have initial knowledge as well as an ability to learn