Intelligent Agents
Chapter 2
Chapter 2 1
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Guidelines and tips
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Intelligent Agents: Understanding Agents, Environments, and Rationality, Study notes of Computer Science
A chapter from artificial intelligence: a modern approach (aima) that introduces the concept of intelligent agents, their interaction with environments, and the concept of rationality. It covers agents and environments, peas (performance measure, environment, actuators, sensors), agent types, and environment types. It also discusses various agent architectures and their differences.
Typology: Study notes
Pre 2010
1 / 27
This page cannot be seen from the preview
Don't miss anything!
Related documents
Partial preview of the text
Download Intelligent Agents: Understanding Agents, Environments, and Rationality and more Study notes Computer Science in PDF only on Docsity!
Intelligent Agents
Chapter 2
Chapter 2
Reminders
Lisp/emacs/AIMA tutorial Assignment 0 (lisp refresher) due 1/
: 11-1 today and Monday, 271 Soda
Chapter 2
Agents and environments
? agent
percepts
sensors
actions
environment
actuators
Agents
include humans, robots, softbots, thermostats, etc.
The
agent function
maps from percept histories to actions:
f
P
∗
→ A
The
agent program
runs on the physical
architecture
to produce
f
Chapter 2
Vacuum-cleaner world
A
B
Percepts: location and contents, e.g.,
[
A, Dirty
]
Actions:
Lef t
Right
Suck
N oOp
Chapter 2
Rationality
Fixed
performance measure
evaluates the
environment sequence
- one point per square cleaned up in time
T
- penalize for– one point per clean square per time step, minus one per move?
k
dirty squares?
A
rational agent
chooses whichever action maximizes the
expected
value of
the performance measure
given the percept sequence to date
Rational
omniscient
- percepts may not supply all relevant information
Rational
clairvoyant
- action outcomes may not be as expected
Hence, rational
successful
Rational
exploration, learning, autonomy
Chapter 2
PEAS
To design a rational agent, we must specify the
task environment
Performance measure Consider, e.g., the task of designing an automated taxi:
Environment
Actuators
Sensors
Chapter 2
Internet shopping agent
Performance measure
Environment
Actuators
Sensors
Chapter 2
Internet shopping agent
Performance measure
price, quality, appropriateness, efficiency
Environment
current and future WWW sites, vendors, shippers
Actuators
display to user, follow URL, fill in form
Sensors
HTML pages (text, graphics, scripts)
Chapter 2
Environment types
Solitaire
Backgammon
Internet shopping
Taxi
Observable
Yes
Yes
No
No
Deterministic
Episodic
Static
Discrete
Single-agent
Chapter 2
Environment types
Solitaire
Backgammon
Internet shopping
Taxi
Observable
Yes
Yes
No
No
Deterministic
Yes
No
Partly
No
Episodic
Static
Discrete
Single-agent
Chapter 2
Environment types
Solitaire
Backgammon
Internet shopping
Taxi
Observable
Yes
Yes
No
No
Deterministic
Yes
No
Partly
No
Episodic
No
No
No
No
Static
Yes
Semi
Semi
No
Discrete
Single-agent
Chapter 2
Environment types
Solitaire
Backgammon
Internet shopping
Taxi
Observable
Yes
Yes
No
No
Deterministic
Yes
No
Partly
No
Episodic
No
No
No
No
Static
Yes
Semi
Semi
No
Discrete
Yes
Yes
Yes
No
Single-agent
Chapter 2
Agent types
Four basic types in order of increasing generality:
- utility-based agents– goal-based agents– reflex agents with state– simple reflex agents
All these can be turned into learning agents
Chapter 2
Simple reflex agents
Agent
Environment
Sensors
is like now What the world should do now What action I
Condition−action rules
Actuators
Chapter 2