AI chapter 2

Artificial Intelligence

Chapter 2 Agents

AI chapter 2

What is an (Intelligent) Agent?• An over-used, over-loaded, and misused term.

Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through its effectors to maximize progress towards its goals.

• Perception• Sensors receive input from environment

• Keyboard clicks• Camera data

• Action• Actuators impact the environment• Move a robotic arm• Generate output for computer display

AI chapter 2

Perception

• Percept•Perceptual inputs at an instant•May include perception of internal

state• Percept Sequence

•Complete history of all prior percepts

• Do you need a percept sequence to play Chess?

AI chapter 2

What is an (Intelligent) Agent?

• PAGE (Percepts, Actions, Goals, Environment)

• Task-specific & specialized: well-defined goals and environment

• The notion of an agent is meant to be a tool for analyzing systems, • It is not a different hardware or new

programming languages

AI chapter 2

• Example: Human mind as network of thousands or millions of agents working in parallel. To produce real artificial intelligence, this school holds, we should build computer systems that also contain many agents and systems for arbitrating among the agents' competing results.

• Distributed decision-making and control

• Challenges:• Action selection: What next action

to choose• Conflict resolution

Intelligent Agents and Artificial Intelligence

sensors

effectors

Agency

AI chapter 2

Agent Types

We can split agent research into two main strands:

• Distributed Artificial Intelligence (DAI) – Multi-Agent Systems (MAS) (1980 – 1990)

• Much broader notion of "agent" (1990’s – present)• interface, reactive, mobile, information

Intelligent Agents

Chapter 2

AI chapter 2

Outline

• Agents and environments• Rationality• PEAS (Performance measure,

Environment, Actuators, Sensors)• Environment types• Agent types

AI chapter 2

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

•••

AI chapter 2

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

•

•

AI chapter 2

Vacuum-cleaner world

• Percepts: location and contents, e.g., [A,Dirty]

• Actions: Left, Right, Suck, NoOp••

AI chapter 2

A vacuum-cleaner agent

• \input{tables/vacuum-agent-function-table}

•

AI chapter 2

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.

•••

AI chapter 2

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.

•

AI chapter 2

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)

•••

AI chapter 2

PEAS

• PEAS: Performance measure, Environment, Actuators, Sensors

• Must first specify the setting for intelligent agent design

• Consider, e.g., the task of designing an automated taxi driver:• Performance measure• Environment• Actuators• Sensors

•

•••

AI chapter 2

PEAS

• 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

•••

•••

AI chapter 2

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)•

AI chapter 2

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

AI chapter 2

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

AI chapter 2

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.

•••

AI chapter 2

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.

•••

AI chapter 2

Environment types

Chess with Chess without Taxi driving

a clock a clockFully observable Yes Yes No Deterministic Strategic Strategic No Episodic No No No Static Semi Yes No Discrete Yes Yes NoSingle agent No No No

• The environment type largely determines the agent design• The real world is (of course) partially observable, stochastic,

sequential, dynamic, continuous, multi-agent

••

AI chapter 2

Agent functions and programs

• 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

••

AI chapter 2

Table-lookup agent

• \input{algorithms/table-agent-algorithm}• 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

•

AI chapter 2

Agent program for a vacuum-cleaner agent

• \input{algorithms/reflex-vacuum-agent-algorithm}

•

AI chapter 2

Agent types

• Four basic types in order of increasing generality:

• Simple reflex agents• Model-based reflex agents• Goal-based agents• Utility-based agents•

AI chapter 2

Simple reflex agents

AI chapter 2

Simple reflex agents

• \input{algorithms/d-agent-algorithm}•

AI chapter 2

Model-based reflex agents

AI chapter 2

Model-based reflex agents

• \input{algorithms/d+-agent-algorithm}•

AI chapter 2

Goal-based agents

•

AI chapter 2

Utility-based agents

AI chapter 2

Learning agents

AI chapter 2

Rational Agents

EnvironmentAgent

percepts

actions

?

Sensors

Effectors

How to design this?

AI chapter 2

Interacting Agents

Collision Avoidance Agent (CAA)• Goals: Avoid running into obstacles• Percepts ?• Sensors?• Effectors ?• Actions ?• Environment: Freeway

Lane Keeping Agent (LKA)• Goals: Stay in current lane• Percepts ?• Sensors?• Effectors ?• Actions ?• Environment: Freeway

AI chapter 2

Interacting Agents

Collision Avoidance Agent (CAA)• Goals: Avoid running into obstacles• Percepts: Obstacle distance, velocity, trajectory• Sensors: Vision, proximity sensing• Effectors: 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

AI chapter 2

Conflict Resolution by Action Selection Agents

• Override: CAA overrides LKA

• Arbitrate: if Obstacle is Close then CAAelse LKA

• Compromise: Choose action that satisfies both

agents

• Any combination of the above

• Challenges: Doing the right thing

AI chapter 2

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 ?• Rational = Optimal ?• Rational = Omniscience ? • Rational = Clairvoyant ?• Rational = Successful ?

AI chapter 2

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 (incl.

• Rational Omniscience(全知 ) its constraints)

• Rational Clairvoyant(有超人力 )

• Rational Successful

AI chapter 2

Behavior and performance of IAs

• Perception (sequence) to Action Mapping: f : P* A• Ideal mapping: specifies which actions an agent ought

to take at any point in time• Description: Look-Up-Table, Closed Form, etc.

• Performance measure: a subjective measure to characterize how successful an agent is (e.g., speed, power usage, accuracy, money, etc.)

• (degree of) Autonomy: to what extent is the agent

able to make decisions and take actions on its own?

AI chapter 2

Look up table

agent

obstacle

sensor

Distance

Action

10 No action

5 Turn left 30 degrees

2 Stop

AI chapter 2

Closed form

• Output (degree of rotation) = F(distance)

• E.g., F(d) = 10/d (distance cannot be less than 1/10)

AI chapter 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 don't only act reactively, but sometimes also proactively

AI chapter 2

How is an Agent different from other software?

• 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

• Agents may migrate from one system to another to access remote resources or even to meet other agents

AI chapter 2

Environment Types

• Characteristics•Accessible vs. inaccessible•Deterministic vs. nondeterministic•Episodic vs. nonepisodic•Hostile vs. friendly•Static vs. dynamic•Discrete vs. continuous

AI chapter 2

Environment Types

• Characteristics•Accessible vs. inaccessible

•Sensors give access to complete state of the environment.

•Deterministic vs. nondeterministic•The next state can be determined based on the current

state and the action.

•Episodic vs. nonepisodic (Sequential)•Episode: each perceive and action pairs•The quality of action does not depend on the previous

episode.

AI chapter 2

Environment Types

• Characteristics•Hostile vs. friendly

•Static vs. dynamic•Dynamic if the environment changes

during deliberation

•Discrete vs. continuous •Chess vs. driving

AI chapter 2

Environment types

Environment

Accessible

Deterministic

Episodic

Static

Discrete

Operating System

VirtualReality

Office Environment

Mars

AI chapter 2

Environment types

Environment

Accessible

Deterministic

Episodic

Static

Discrete

Operating System

Yes Yes No No Yes

VirtualReality

Office Environment

Mars

AI chapter 2

Environment types

Environment

Accessible

Deterministic

Episodic

Static

Discrete

Operating System

Yes Yes No No Yes

VirtualReality

Yes Yes Yes/no

No Yes/no

Office Environment

Mars

AI chapter 2

Environment types

Environment

Accessible

Deterministic

Episodic

Static

Discrete

Operating System

Yes Yes No No Yes

VirtualReality

Yes Yes Yes/no

No Yes/no

Office Environment

No No No No No

Mars

AI chapter 2

Environment types

Environment

Accessible

Deterministic

Episodic

Static

Discrete

Operating System

Yes Yes No No Yes

VirtualReality

Yes Yes Yes/no

No Yes/no

Office Environment

No No No No No

Mars No Semi No Semi No

The environment types largely determine the agent design.

AI chapter 2

Structure of Intelligent Agents

• Agent = architecture + program

• Agent program: the implementation of f : P* A, the agent’s perception-action mapping

function Skeleton-Agent(Percept) returns Actionmemory 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, beobot, etc.)

AI chapter 2

Using a look-up-table to encode f : P* A

• Example: Collision Avoidance• Sensors: 3 proximity sensors• Effectors: Steering Wheel, Brakes

• How to generate?• How large?• How to select action?

agent

obstacle

sensors

AI chapter 2

Using a look-up-table to encode f : P* A

• Example: Collision Avoidance• Sensors: 3 proximity sensors • Effectors: Steering Wheel, Brakes

• How to generate: for each p Pl Pm Pr

generate an appropriate action, a S B

• How large: size of table = #possible percepts times # possible actions = |Pl | |Pm| |Pr| |S| |B|E.g., P = {close, medium, far}3

A = {left, straight, right} {on, off}then size of table = 27*3*2 = 162

• How to select action? Search.

agent

obstaclesensors

AI chapter 2

Agent types

• Reflex agents• Reflex agents with internal

states• Goal-based agents• Utility-based agents

AI chapter 2

Agent types

• Reflex agents •Reactive: No memory

• Reflex agents with internal states•W/o previous state, may not be able to

make decision •E.g. brake lights at night.

• Goal-based agents•Goal information needed to make decision

AI chapter 2

Agent types

• Utility-based agents•How well can the goal be achieved

(degree of happiness)

•What to do if there are conflicting goals?•Speed and safety

•Which goal should be selected if several can be achieved?

AI chapter 2

Reflex agents

AI chapter 2

Reactive agents

• Reactive agents do not have internal symbolic models. • Act by stimulus-response to the current state of the

environment. • Each reactive agent is simple and interacts with others in a

basic way. • Complex patterns of behavior emerge from their interaction.

• Benefits: robustness, fast response time • Challenges: scalability, how intelligent?

and how do you debug them?

AI chapter 2

Reflex agents w/ state

AI chapter 2

Goal-based agents

AI chapter 2

Utility-based agents

AI chapter 2

Mobile agents

• Programs that can migrate from one machine to another. • Execute in a platform-independent execution environment. • Require agent execution environment (places). • Mobility not necessary or sufficient condition for

agenthood. • Practical but non-functional advantages:

• Reduced communication cost (eg, from PDA) • Asynchronous computing (when you are not connected)

• Two types: • One-hop mobile agents (migrate to one other place) • Multi-hop mobile agents (roam the network from place to place)

• Applications: • Distributed information retrieval. • Telecommunication network routing.

AI chapter 2

Mobile agents

• Programs that can migrate from one machine to another.

• Execute in a platform-independent execution environment.

• Require agent execution environment (places).

• Mobility not necessary or sufficient condition for agenthood.

A mail agent

AI chapter 2

Mobile agents

• Practical but non-functional advantages: •Reduced communication cost (e.g. from PDA) •Asynchronous computing (when you are not

connected)

• Two types: •One-hop mobile agents (migrate to one other

place) •Multi-hop mobile agents (roam the network from

place to place)

AI chapter 2

Mobile agents

• Applications: •Distributed information retrieval. •Telecommunication network routing.

AI chapter 2

Information agents

• Manage the explosive growth of information. • Manipulate or collate information from many

distributed sources. • Information agents can be mobile or static.

• Examples: • BargainFinder comparison shops among Internet stores for

CDs • FIDO the Shopping Doggie (out of service)• Internet Softbot infers which internet facilities (finger, ftp,

gopher) to use and when from high-level search requests.

• Challenge: ontologies for annotating Web pages (eg, SHOE).

AI chapter 2

Summary

• Intelligent Agents:• Anything that can be viewed as perceiving its environment

through sensors and acting upon that environment through its effectors to maximize progress towards its goals.

• PAGE (Percepts, Actions, Goals, Environment)• Described as a Perception (sequence) to Action Mapping: f : P*

A• Using look-up-table, closed form, etc.

• Agent Types: Reflex, state-based, goal-based, utility-based

• Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date