CS:4420 Artificial Intelligence Spring 2019 Intelligent Agents Cesare Tinelli The University of Iowa Copyright 2004–19, Cesare Tinelli and Stuart Russell a a These notes were originally developed by Stuart Russell and are used with permission. They are copyrighted material and may not be used in other course settings outside of the University of Iowa in their current or modified form without the express written consent of the copyright holders. CS:4420 Spring 2019 – p.1/37
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
CS:4420 Artificial Intelligence
Spring 2019
Intelligent Agents
Cesare Tinelli
The University of Iowa
Copyright 2004–19, Cesare Tinelli and Stuart Russell a
aThese notes were originally developed by Stuart Russell and are used with permission. They are
copyrighted material and may not be used in other course settings outside of the University of Iowa in their
current or modified form without the express written consent of the copyright holders.
CS:4420 Spring 2019 – p.1/37
Readings
• Chap. 2 of [Russell and Norvig, 3rd edition]
CS:4420 Spring 2019 – p.2/37
Intelligent Agents
• An agent is a system that perceives its environment throughsensors and acts upon that environment through effectors
• A rational agent is an agent whose acts try to maximize someperformance measure
CS:4420 Spring 2019 – p.3/37
Agents and Environments
Agent Sensors
Actuators
Environm
ent
Percepts
Actions
?
Agents include humans, robots, softbots, thermostats, etc.
CS:4420 Spring 2019 – p.4/37
Agents as Mappings
An agent can be seen as a mapping between percept sequences andactions.
f : Percept∗ −→ Action
An agent program runs on a physical architecture to produce f
The less an agents relies on its built-in knowledge, as opposed to thecurrent percept sequence and acquired knowledge, the moreautonomous it is
CS:4420 Spring 2019 – p.5/37
Vacuum-cleaner world
A B
Percepts: location and contents, e.g., [A,Dirty ]
Actions: Left , Right , Suck , NoOp
CS:4420 Spring 2019 – p.6/37
A vacuum-cleaner agent
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
.
.
....
function Reflex-Vacuum-Agent( [location,status]) returns action
if status = Dirty then return Suck
else if location = A then return Right
else if location = B then return Left
CS:4420 Spring 2019 – p.7/37
More Examples of Artificial Agents
Agent Type Percepts Actions Goals Environment
Medical diagnosissystem
Symptoms,findings, patient’sanswers
Questions, tests,treatments
Healthy patient,minimize costs
Patient, hospital
Satellite imageanalysis system
Pixels of varyingintensity, color
Print acategorization ofscene
Correctcategorization
Images fromorbiting satellite
Part-picking robot Pixels of varyingintensity
Pick up parts andsort into bins
Place parts incorrect bins
Conveyor beltwith parts
Refinery controller Temperature,pressure readings
Open, closevalves; adjusttemperature
Maximize purity,yield, safety
Refinery
Interactive Englishtutor
Typed words Print exercises,suggestions,corrections
Maximizestudent’s score ontest
Set of students
CS:4420 Spring 2019 – p.8/37
Rational Agents
The rationality of an agent depends on
• the performance measure, defining the agent’s degree of success
• the percept sequence, listing all the things perceived by the agent
• the agent’s knowledge of the environment
• the actions that the agent can perform
For each possible percept sequence, an ideal rational agent doeswhatever possible to maximize its performance, based on:
• the percept sequence and
• its internal knowledge
CS:4420 Spring 2019 – p.9/37
Rationality
• What is the right function?
• Can it be implemented in a small agent program?
• Fixed performance measure evaluates the environment sequence
• 1 point per square cleaned up in time T?
• 1 point per clean square per time step, minus one per move?
• penalize for > k dirty squares?
CS:4420 Spring 2019 – p.10/37
Rationality
• What is the right function?
• Can it be implemented in a small agent program?
• Fixed performance measure evaluates the environment sequence
• 1 point per square cleaned up in time T?
• 1 point per clean square per time step, minus one per move?
• penalize for > k dirty squares?
Note:
Rational 6= omniscient
Rational 6= clairvoyant
Rational 6= successful
Rational =⇒ exploration, learning, autonomyCS:4420 Spring 2019 – p.10/37
PEAS
To design a rational agent, we must specify the task environment
Consider, e.g., the task of designing a driverless taxi:
• Performance measure?
• Environment?
• Actuators?
• Sensors?
CS:4420 Spring 2019 – p.11/37
PEAS
To design a rational agent, we must specify the task environment
Consider, e.g., the task of designing a driverless taxi: