1. CSC AI
Intelligent Agents

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

3. Agents
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
Human agent:
eyes, ears, and other organs for sensors;
Hands, legs, mouth, and other body parts for actuators
Robotic agent:
cameras and infrared range finders for sensors;
various motors for actuators

4. Agents
Percept
Agent's perceptual inputs at any given instant
Percept Sequence
The complete history of everything the agent has ever perceived
An agent's choice of action at any given instant can depend on the entire percept sequence observed to date

5. Agents and environments
The agent function maps from percept histories to actions:
[f: P*  A]
The agent program runs on the physical architecture to produce f
agent = architecture + program

6. Vacuum-cleaner world Percepts: location and contents, e.g., [A, Dirty]
Actions: Left, Right, Suck, NoOp

7. A vacuum-cleaner agent
Percept sequence
Action
[A, Clean]
Right
[A, Dirty]
Suck
[B, Clean]
Left
[B, Dirty]
[A, Clean], [A, Clean]
[A, Clean], [A, Dirty]
[A, Clean], [A, Clean], [A, Clean]
[A, Clean], [A, Clean], [A, Dirty]
Partial tabulation of a simple agent function for the vacuum-cleaner world

8. 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.

9. Rational agents
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.

10. Rational agents
Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration)
An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt)

11. Agent Design
PEAS: Performance measure, Environment, Actuators, Sensors( percept)
Must first specify the setting for intelligent agent design
Consider, e.g., the task of designing an automated taxi driver:
Performance measure
Environment
Actuators
Percepts

12. Agent Design
Consider, e.g., 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
percepts: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard

13. PEAS Agent: 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)

14. Agent Design 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

15. 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

16. Environment types
Accessible (vs. inaccessible): An agent's sensors give it access to the complete state of the environment at each point in time.
Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent.
Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself.

17. Environment types
Static (vs. dynamic): The environment is unchanged while an agent is deliberating. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does)
Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions.
Single agent (vs. multiagent): An agent operating by itself in an environment.

18. Environment types Chess with Chess without Taxi driving
a clock a clock
Accessible Yes Yes No
Deterministic Strategic Strategic No
Episodic No No No
Static Semi Yes No
Discrete Yes Yes No
The environment type largely determines the agent design
The real world is (of course) partially accessible, stochastic, sequential, dynamic, continuous, multi-agent

19. INTELLIGENT AGENTS A Classification
Table Driven agents
simple reflex agents وكيل يعتمد على ردة فعل بسيطة
model-based reflex agents وكيل يعتمد على نموذج وكيل مع ردة فعل
goal-based agents وكيل ذو هدف معين
utility-based agents وكيل قائم على التفضيل

20. Impractical 1. Table Driven Agent. table lookup for entire history
current state of decision process
Impractical
table lookup
for entire history

21. Fast but too simple 2. Simple reflex agents NO MEMORY
Fails if environment
is partially observable
example: vacuum cleaner world

22. Simple reflex agents
With perception
Environment
Agent
see
action

23. Example: Simple reflex agents
Percept Action
At A, A Dirty Vacuum
At A, A Clean Move Left
At B, B Dirty Vacuum
At B, B Clean Move right

24. Simple reflex agents Simple but Limited functionality: Good when
Environment is fully observable
Condition -action rules have predicted all necessary actions.

25. 3. Model-based reflex agents
description of
current world state
Model the state of the world by:
modeling how the world changes
how it’s actions change the world
This can work even with partial information
It’s is unclear what to do
without a clear goal

26. Model-based reflex agents
With internal states
Agent
see
action
Predict
state
Environment

27. Model-based reflex agents
Model-based reflex agents are made to deal with partial accessibility; they do this by keeping track of the part of the world it can see now. It does this by keeping an internal state that depends on what it has seen before so it holds information on the unobserved aspects of the current state.

28. Model-based reflex agents
Action may depend on history or unperceived aspects of the world.
Need to maintain internal world model.
Example:
Agent: robot vacuum cleaner
Environment: dirty room, furniture.
Model: map of room, which areas already cleaned.
Sensor/model tradeoff.

29. 4. Goal-based agents
Goals provide reason to prefer one action over the other.
We need to predict the future: we need to plan & search

30. Goal-based agents Agent Goals Decision see state Environment action
Predict
state
Environment

31. Goal-based agents
In life, in order to get things done we set goals for us to achieve, this pushes us to make the right decisions when we need to. A simple example would be the shopping list; our goal is to pick up every thing on that list. This makes it easier to decide if you need to choose between milk and orange juice because you can only afford one. As milk is a goal on our shopping list and the orange juice is not we chose the milk.

32. Goal-based agents Agents so far have fixed, implicit goals.
We want agents with variable goals.
Example:
Agent: robot maid
Environment: house & people.
Goals: clean clothes, tidy room, table laid, etc

33. 5. Utility-based agents
Some solutions to goal states are better than others.
Which one is best is given by a utility function.
Which combination of goals is preferred?

34. Utility-based agents
Just having goals isn’t good enough because often we may have several actions which all satisfy our goal so we need some way of working out the most efficient one. A utility function maps each state after each action to a real number representing how efficiently each action achieves the goal. This is useful when we either have many actions all solving the same goal or when we have many goals that can be satisfied and we need to choose an action to perform

35. Utility-based agents Agents so far have had a single goal.
Agents may have to juggle conflicting goals.
Need to optimize utility over a range of goals.
Utility: measure of goodness (a real number).
Combine with probability of success to get expected utility.
Example:
Agent: automatic car.
Environment: roads, vehicles, signs, etc.
Goals: stay safe, reach destination, be quick, obey law, save fuel, etc.

36. Learning agents How does an agent improve over time?
By monitoring it’s performance and suggesting
better modeling, new action rules, etc.
Evaluates
current
world
state
changes
action
rules
“old agent”=
model world
and decide on
actions
to be taken
suggests
explorations

37. Multi-Agent Systems Agent Agent ENVIRONMENT ENVIRONMENT Agent Agent

38. Multiagent Systems Features
Interaction
Communication languages
Protocols
Policies
Co-ordination
Co-operation
Collaboration : Shared goals
Negotiation

39. End of Lecture