Download Artificial Intelligence Intelligent Autonomous Agents 1

Artificial Intelligence
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Lecture #2- Intelligent Agents
Artificial Intelligence
ICS461
Fall 2010
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Nancy E. Reed
[email protected]
What is an intelligent agent?
Agents and environments
Rationality
PEAS (Performance measure,
measure Environment,
Environment
Actuators, Sensors)
Environment types
Agent types
Reference – Ch. 2
http://aima.cs.berkeley.edu/index.html
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What is an Autonomous Agent?
Agents
ƒ An agent is anything that can be viewed as
perceiving its environment through sensors and
acting upon that environment through actuators
ƒ Human agent: eyes, ears, and other organs for
sensors; hands, legs, mouth, and other body parts for
actuators
ƒ Robotic agent: cameras and infrared range finders
for sensors; various motors for actuators
Interacts with its environment
1. can sense its environment
2. make decisions
decisions, and
3. take action
percepts
p
p
environment
Sensors: eyes
eyes, sonar
sonar, …
agent
actions
Effectors: hands, motors, …
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(Physical) Types of Autonomous Agents
Agents
Biological
Agents
Software Softbots
Artificial Life
Virus
Hardware
- Robots
Task specific
Software Agents - Softbots
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“Live” inside computers and networks
Percepts: text, images, bits,
Actions: display information, send messgaes, …
Examples: simulated pilots, Electric Elves, chat room
‘bots’
bots , MS “paperclip”
paperclip , …
percepts
environment
keyboards, …
agent
Entertainment
actions
Intelligent Autonomous Agents
Sensors: networks,
Effectors: networks,
monitors …
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Robotic Agents
Agents and environments
ƒ Physical hardware interacts with the
environment
ƒ Percepts: images, sound, pressure,
acceleration, …
ƒ Actions: movement, sound, light, …
percepts
environment
Sensors: cameras,
microphones, sonar, …
ƒ The agent program runs on the physical architecture to
produce f
ƒ agent = architecture + program
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agent
actions
ƒ The
Th agentt function
f ti maps from
f
perceptt histories
hi t i to
t actions:
ti
[f: P* Æ A]
Effectors: motors,
speakers, LEDs …
Tiny Vacuum-cleaner World
A vacuum-cleaner agent
ƒ \input{tables/vacuum-agent-functiontable}
ƒ http://aima.cs.berkeley.edu/lisp/doc/overvi
ew.html
ƒ Percepts: location and contents, e.g.,
[A,Dirty]
ƒ Actions: Left, Right, Suck, NoOp
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A vacuum-cleaner agent
Rational Agents
ƒ An agent should strive to "do the right thing",
based on what it can perceive and the actions
it can perform.
ƒ The right action is the one that will cause the
agent to be most successful
ƒ Performance measure: An objective
criterion for success of an agent's behavior
ƒ E.g., performance measure of a vacuumcleaner agent could be amount of dirt cleaned
up, amount of time taken, amount of
electricity consumed, amount of noise
generated, etc.
Intelligent Autonomous Agents
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Artificial Intelligence
Rational agents
ƒ 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.
PEAS Description
ƒ PEAS: Performance measure,
Environment, Actuators, Sensors
ƒ Must first specify the setting for intelligent
agent design
g , the task of designing
g g an
Consider,, e.g.,
automated taxi driver:
1. Performance measure
2. Environment
3. Actuators
4. Sensors
Rational agents
ƒ Rationality is distinct from omniscience
(all-knowing with infinite knowledge)
ƒ Agents can perform actions in order to
modify future percepts so as to obtain
useful information (information
gathering, exploration)
ƒ An agent is autonomous if its behavior is
determined by its own experience (with
ability to learn and adapt)
Taxi Example - PEAS
ƒ First specify the setting for agent design
ƒ Consider, e.g., the task of designing an
automated taxi driver:
ƒ Performance measure: Safe, fast, legal,
comfortable trip, maximize profits
ƒ Environment: Roads
Roads, other traffic
traffic,
pedestrians, customers
ƒ Actuators: Steering wheel, accelerator,
brake, signal, horn
ƒ Sensors: Cameras, sonar, speedometer,
GPS, odometer, engine sensors,
keyboard
Medical Example: PEAS
Example Robot : PEAS
ƒ Agent: Medical diagnosis system
ƒ Performance measure: Healthy patient,
minimize costs, lawsuits
ƒ Environment: Patient,, hospital,
p , staff
ƒ Actuators: Screen display (questions,
tests, diagnoses, treatments, referrals)
ƒ Sensors: Keyboard (entry of symptoms,
findings, patient's answers)
ƒ Agent: Part-picking robot
ƒ Performance measure: Percentage of
parts in correct bins
ƒ Environment: Conveyor
y belt with parts,
p ,
bins
ƒ Actuators: Jointed arm and hand
ƒ Sensors: Camera, joint angle sensors
Intelligent Autonomous Agents
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Artificial Intelligence
English Tutor Example: PEAS
Environment Characteristics (I)
ƒ Fully observable (vs. partially observable): An
agent's sensors give it access to the complete state of
the environment at each point in time.
ƒ Deterministic (vs. stochastic): The next state of the
environment is completely determined by the current
g
((If the
state and the action executed byy the agent.
environment is deterministic except for the actions of
other agents, then the environment is strategic)
ƒ 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.
ƒ Agent: Interactive English tutor
ƒ Performance measure: Maximize
student's score on test
ƒ Environment: Set of students
ƒ Actuators: Screen display (exercises,
suggestions, corrections)
ƒ Sensors: Keyboard
Environment Characteristics (II)
Environment Type Examples
ƒ 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)
ƒ Discrete (vs. continuous): A limited number of
distinct, clearly defined percepts and actions.
ƒ Single agent (vs. multiagent): An agent operating by
itself in an environment.
Fully observable
Deterministic
Episodic
Static
Discrete
Single agent
Chess with
a clock
Chess without Taxi driving
a clock
Yes
Strategic
No
Semi
Yes
No
Yes
Strategic
No
Yes
Yes
No
ƒ The environment type largely determines the agent design
ƒ The real world is (of course) partially observable,
stochastic, sequential, dynamic, continuous, multi-agent
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Quiz
Intelligent Autonomous Agents
No
No
No
No
No
No
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Quiz
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Artificial Intelligence
Agent functions and programs
Table-lookup agent
ƒ An agent is completely specified by the
agent function mapping percept
sequences to actions
ƒ One agent function (or a small
equivalence class) is rational
ƒ Aim: find a way to implement the
rational agent function concisely
ƒ \input{algorithms/table-agent-algorithm}
ƒ Drawbacks:
•
•
•
•
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A vacuum-cleaner agent
Agent types
Huge table
Take a long time to build the table
No autonomy
Even with learning, need a long time to learn the
table entries
Agent program for a vacuum-cleaner
agent
ƒ \input{algorithms/reflex-vacuum-agentalgorithm}
Simple reflex agents
ƒ Four basic types in order of increasing
generality:
ƒ Simple reflex agents
ƒ Model-based reflex agents
g
ƒ Goal-based agents
ƒ Utility-based agents
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Artificial Intelligence
Simple reflex agents
Model-based reflex agents
ƒ \input{algorithms/d-agent-algorithm}
Model-based reflex agents
Goal-based agents
ƒ \input{algorithms/d+-agent-algorithm}
Utility-based agents
Intelligent Autonomous Agents
Learning agents
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Summary
Intelligent Autonomous Agents
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