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Dr. Alaa Sagheer 2010-2011
Chapter Chapter 22
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Agents and environments Rationality PEAS (Performance measure,
Environment, Actuators, Sensors) Environments types Agents types
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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
various motors for actuators Robotic agent: cameras and infrared
range finders for sensors;Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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 + programArtificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Environment: square A and B Percepts: [location and content] e.g.
[A, Dirty] Actions: left, right, suck, and no-op
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Percept sequence Action
[A,Clean] Right
[A, Dirty] Suck
[B, Clean] Left
[B, Dirty] Suck
[A, Clean],[A, Clean] Right
[A, Clean],[A, Dirty] Suck… …
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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?
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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)
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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 successful 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.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
What is rational at a given time depends on four things:› Performance measure› Prior environment knowledge› Actions› Percept sequence to date (sensors)
A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date and prior environment knowledge
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Rationality omniscience› An omniscient agent knows the
actual outcome of its actions. Rationality perfection
› Rationality maximizes expected performance, while perfection maximizes actual performance.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
The proposed definition requires:› Information gathering/exploration
To maximize future rewards› Learn from percepts
Extending prior knowledge› Agent autonomy
Compensate for incorrect prior knowledge
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Must first specify the setting for intelligent 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› Sensors: Cameras, sonar, speedometer, GPS,
odometer, engine sensors, keyboard
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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)
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Agent: Interactive English tutor Performance measure: Maximize
student's score on test Environment: Set of students Actuators: Screen display (exercises,
suggestions, corrections) Sensors: Keyboard
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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.Artificial Intelligence Ch2: Intelligent Agents
Dr. Alaa Sagheer
Static (vs. dynamic): The environment is unchanged while an agent is deliberating. (The environment is semi dynamic 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.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable??
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??
Fully vs. partially observable: an environment is full observable when the sensors can detect all aspects that are relevant to the choice of action.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??
Fully vs. partially observable: an environment is full observable when the sensors can detect all aspects that are relevant to the choice of action.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??
Deterministic vs. stochastic: if the next environment state is completely determined by the current state the executed action then the environment is deterministic.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic??
Static??
Discrete??
Single-agent??
Deterministic vs. stochastic: if the next environment state is completely determined by the current state the executed action then the environment is deterministic.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic??
Static??
Discrete??
Single-agent??
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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static??
Discrete??
Single-agent??
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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static??
Discrete??
Single-agent??
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.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete??
Single-agent??
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.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Internet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete??
Single-agent??
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.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete?? YES YES YES NO
Single-agent??
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.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete?? YES YES YES NO
Single-agent??
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?
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Solitaire Backgammom Intenet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete?? YES YES YES NO
Single-agent?? YES NO NO NO
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?
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
An agent is completely specified by the agent function mapping percept sequences to actions
One agent function (or a small equivalence class) is rational
Aim: find a way to implement the rational agent function concisely
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Function TABLE-DRIVEN_AGENT(percept) returns an action
static: percepts, a sequence initially emptytable, a table of actions, indexed by percept sequence
append percept to the end of perceptsaction LOOKUP(percepts, table)return action
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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.
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Select action on the basis of only the current percept ignoring the rest of history.› 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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
4T
function SIMPLE-REFLEX-AGENT(percept) returns an action
static: rules, a set of condition-action rulesstate 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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer
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
Artificial Intelligence Ch2: Intelligent Agents Dr. Alaa Sagheer