1. CSCE 580 Artificial Intelligence Ch.2: Intelligent Agents
Fall 2008
Marco Valtorta

2. Acknowledgment
The slides are based on the textbook [AIMA] and other sources, including other fine textbooks
The other textbooks I considered are:
David Poole, Alan Mackworth, and Randy Goebel. Computational Intelligence: A Logical Approach. Oxford, 1998
A second edition (by Poole and Mackworth) is under development. Dr. Poole allowed us to use a draft of it in this course
Ivan Bratko. Prolog Programming for Artificial Intelligence, Third Edition. Addison-Wesley, 2001
The fourth edition is under development
George F. Luger. Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Sixth Edition. Addison-Welsey, 2009

3. Agents
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
Actuators are sometimes called effectors
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 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
Figure 2.1 in AIMA-95 is on the left; in AIMA-02 on the right; Figure 1.3 in [P] below

5. The Role of Representation
Choosing a representation involves balancing conflicting objectives
Different tasks require different representations
Representations should be expressive (epistemologically adequate) and efficient (heuristically adequate)
[P], fig. 1.4; we will discuss representations later in the course

6. The Vacuum-Cleaner World
Environment: square A and B
Percepts: [location and content] e.g. [A, Dirty]
Actions: left, right, suck, and no-op

7. The Vacuum-Cleaner World
Percept sequence
Action
[A,Clean]
Right
[A, Dirty]
Suck
[B, Clean]
Left
[B, Dirty]
[A, Clean],[A, Clean]
[A, Clean],[A, Dirty] …
Figs. 2.2 and 2.3 [AIMA]. Fig,.2.3 shows an agent function (partial): a function from percept sequences to actions.
Note that a lot of information (e.g. how full the canister or bag is) is abstracted away

8. The Vacuum-Cleaner World
function REFLEX-VACUUM-AGENT ([location, status]) return an action
if status == Dirty then return Suck
else if location == A then return Right
else if location == B then return Left
What is the right function? Can it be implemented in a small agent program?
Figure 2.8 [AIMA]: an implementation of the agent function tabulated in Figure 2.3 [AIMA]

9. 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.

10. 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.

11. Rational Agents
Rationality is distinct from omniscience (all-knowing with infinite knowledge)
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)

12. The Concept of Rationality
A rational agent is one that does the right thing
Every entry in the table is filled out correctly
What is the right thing?
Approximation: the most succesful agent
Measure of success?
Performance measure should be objective
E.g. the amount of dirt cleaned within a certain time
E.g. how clean the floor is …
Better to design performance measures according to what is wanted in the environment instead of how the agents should behave.

13. Rationality Performance measure Prior environment knowledge Actions
What is rational at a given time depends on four things:
Performance measure
Prior environment knowledge
Actions
Percept sequence to date (sensors)
DEF: A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date and prior environment knowledge

14. Rationality Rationality  omniscience
An omniscient agent knows the actual outcome of its actions.
Rationality  perfection
Rationality maximizes expected performance, while perfection maximizes actual performance.

15. Rationality The proposed definition requires:
Information gathering/exploration
To maximize future rewards
Learn from percepts
Extending prior knowledge
Dung beetle and sphex wasp plans
Agent autonomy
Compensate for incorrect prior knowledge

16. PEAS
Task environment
a problem for which a rational agents is the solution
specified by the PEAS description:
Performance measure, Environment, Actuators, Sensors
Must first specify the setting for intelligent agent design
Consider, e.g., the task of designing an automated taxi:
Performance measure
Environment
Actuators
Sensors
AIMA-95 uses PAGE: Percepts (=Sensors), Actions (=Actuators), Goals (=Performance Measure), Environment. It is states that “the goals do not have to be represented within the agent; they simply describe the performance measure by which the agent design will be judged.”

17. PEAS for Automated Taxi
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

18. PEAS for a 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)

19. PEAS for a 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

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

21. Environment Types
Fully observable (vs. partially observable): 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. (If the environment is deterministic except for the actions of other agents, then the environment is strategic)
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

22. 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

23. Environment Types
Fully vs. partially observable: an environment is full observable when the
sensors can detect all aspects that are relevant to the choice of action.
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??
See Figure 2.6 [AIMA] for more examples

24. Environment Types
Fully vs. partially observable: an environment is full observable when the
sensors can detect all aspects that are relevant to the choice of action.
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??

25. Environment Types
Deterministic vs. stochastic: if the next environment state is completely
determined by the current state the executed action then the environment is
deterministic.
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??

26. Environment Types
Deterministic vs. stochastic: if the next environment state is completely
determined by the current state the executed action then the environment is
deterministic.
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
YES NO
Episodic??
Static??
Discrete??
Single-agent??

27. Environment Types
Episodic vs. sequential: In an episodic environment the agent’s experience
can be divided into atomic steps where the agents perceives and then performs
A single action. The choice of action depends only on the episode itself
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
YES NO
Episodic??
Static??
Discrete??
Single-agent??

28. Environment Types
Episodic vs. sequential: In an episodic environment the agent’s experience
can be divided into atomic steps where the agents perceives and then performs
A single action. The choice of action depends only on the episode itself
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
YES NO
Episodic??
Static??
Discrete??
Single-agent??

29. changes even when the environment remains the same.
Environment Types
Static vs. dynamic: If the environment can change while the agent is choosing
an action, the environment is dynamic. Semi-dynamic if the agent’s performance
changes even when the environment remains the same.
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
YES NO
Episodic??
Static??
Discrete??
Single-agent??
If the agent performance score changes as time passes, the environment is semi-dynamic.

30. changes even when the environment remains the same.
Environment Types
Static vs. dynamic: If the environment can change while the agent is choosing
an action, the environment is dynamic. Semi-dynamic if the agent’s performance
changes even when the environment remains the same.
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
YES NO
Episodic??
Static??
SEMI
Discrete??
Single-agent??
If the agent performance score changes as time passes, the environment is semi-dynamic.

31. Environment Types
Discrete vs. continuous: This distinction can be applied to the state of the
environment, the way time is handled and to the percepts/actions of the agent.
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
YES NO
Episodic??
Static??
SEMI
Discrete??
Single-agent??
If the agent performance score changes as time passes, the environment is semi-dynamic.

32. Environment Types
Discrete vs. continuous: This distinction can be applied to the state of the
environment, the way time is handled and to the percepts/actions of the agent.
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
YES NO
Episodic??
Static??
SEMI
Discrete??
Single-agent??
If the agent performance score changes as time passes, the environment is semi-dynamic.

33. Environment Types
Single vs. multi-agent: Does the environment contain other agents who
are also maximizing some performance measure that depends on the
current agent’s actions?
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
YES NO
Episodic??
Static??
SEMI
Discrete??
Single-agent??
If the agent performance score changes as time passes, the environment is semi-dynamic.

34. Environment Types
Single vs. multi-agent: Does the environment contain other agents who
are also maximizing some performance measure that depends on the
current agent’s actions?
Solitaire
Backgammom
Intenet shopping
Taxi
Observable??
FULL
PARTIAL
Deterministic??
YES NO
Episodic??
Static??
SEMI
Discrete??
Single-agent??
If the agent performance score changes as time passes, the environment is semi-dynamic.

35. Environment Types The simplest environment is
Fully observable, deterministic, episodic, static, discrete and single-agent.
Most real situations are:
Partially observable, stochastic, sequential, dynamic, continuous and multi-agent.

36. Agent Types Four basic types in order of increasing generality:
Simple reflex agents
Model-based reflex agents
Goal-based agents
Utility-based agents
All these can be turned into learning agents

37. Agent Types Function TABLE-DRIVEN_AGENT(percept) returns an action
static: percepts, a sequence initially empty
table, a table of actions, indexed by percept sequence
append percept to the end of percepts
action  LOOKUP(percepts, table)
return action
This approach is doomed to failure

38. Simple Reflex Agents E.g. the vacuum-agent If dirty then suck
Select action on the basis of only the current percept
E.g. the vacuum-agent
Large reduction in possible percept/action situations (example on next page)
Implemented through condition-action rules
If dirty then suck

39. The Vacuum-Cleaner World
function REFLEX-VACUUM-AGENT ([location, status]) return an action
if status == Dirty then return Suck
else if location == A then return Right
else if location == B then return Left
Reduction from 4T to 4 entries

40. Simple Reflex Agent Function
function SIMPLE-REFLEX-AGENT(percept) returns an action
static: rules, a set of condition-action rules
state  INTERPRET-INPUT(percept)
rule  RULE-MATCH(state, rule)
action  RULE-ACTION[rule]
return action
Will only work if the environment is fully observable
E.g., determination of braking in cars without centrally mounted brake lights

41. Model-Based Reflex Agents
To tackle partially observable environments.
Maintain internal state
Over time update state using world knowledge
How does the world change.
How do actions affect world.
 Model of World

42. Model-Based Agent Function
function REFLEX-AGENT-WITH-STATE(percept) returns an action
static: rules, a set of condition-action rules
state, a description of the current world state
action, the most recent action.
state  UPDATE-STATE(state, action, percept)
rule  RULE-MATCH(state, rule)
action  RULE-ACTION[rule]
return action

43. Model-Based Goal-Based agents
The agent needs a goal to know which situations are desirable
Things become difficult when long sequences of actions are required to find the goal
Typically investigated in search and planning research
Major difference: future is taken into account
Is more flexible since knowledge is represented explicitly and can be manipulated

44. Utility-Based Agents Some are better, have a higher utility
Certain goals can be reached in different ways
Some are better, have a higher utility
Utility function maps a (sequence of) state(s) onto a real number.
Improves on goals:
Selecting between conflicting goals
Select appropriately between several goals based on likelihood of success

45. Learning Agents Yet the origin of these programs is not provided
All previous agent programs describe methods for selecting actions
Yet the origin of these programs is not provided
Learning mechanisms can be used to perform this task
Teach them instead of instructing them
Advantage is the robustness of the program toward initially unknown environments

46. Learning Agents
Learning element: introduce improvements in performance element.
Critic provides feedback on agents performance based on fixed performance standard
Performance element: selecting actions based on percepts
Corresponds to the previous agent programs
Problem generator: suggests actions that will lead to new and informative experiences
Exploration