Download Four approaches to defining AI systems Cognitive science Rational

Four approaches to
defining AI systems
CS 4633/6633: Artificial Intelligence
Intelligent Agents
(chapter 2)
Systems that think like
humans
Systems that think
rationally.
Systems that act like
humans.
Systems that act
rationally.
Cognitive science
Rational agent approach to AI
An interdisciplinary field of study that
combines AI and psychology
● It builds computer systems that simulate
human mental processes, as a way of
understanding how the human mind works
● Based on experimental investigation of
humans (and animals)
Russell and Norvig define AI as the study and
construction of rational (intelligent) agents.
● This is a broader definition of intelligence than
human intelligence (we should not expect
computer intelligence to be the same as human
intelligence anymore than we expect airplanes
to fly the same as birds)
●
●
●
Agent
An agent is anything that can be viewed as
perceiving its environment through sensors and
acting upon that environment through effectors.
Intelligent behavior is also a broader concept
than intelligent thought (intelligent behavior
may depend on reflex, not thinking)
Rational agent
A rational agent is something that attempts
to achieve its goals, given its beliefs
● A rational agent is capable of autonomous
behavior
● Examples: robots, softbots (software agents)
● How are agents different from traditional
computer systems?
●
A simple reflex agent
Types of agent designs
(in order of increasing generality)
What the world
is like now
– Simple reflex agents
– Agents with memory to keep track of the world
●
Sensors
Agent
Reactive agents (pre-programmed behavior)
Deliberative agents (can think and plan)
Condition-action rules
– Goal-based agents
– Utility-based agents
What action I
should do now
Environment
●
Effectors
Condition-action rules map current percepts to actions
A reflex agent with internal state
An agent with explicit goals
Sensors
is like now.
What my actions do
Condition-action rules
Agent
What action should
I do now
Environment
What the world
How the world evolves
How the world evolves
What my actions do
Goals
Agent
What the world
is like now
What it will be like if
I do action A
What action I
should do now
Environment
State
Sensors
State
Effectors
Effectors
Internal provides a form of memory that lets an agent
keep track of what has happened in the past
The agent evaluates alternative actions to determine
Which is most helpful in achieving its explicit goals
A utility-based agent
Some example agents
Sensors
State
What my actions do
Utility
What it will be like if
I do action A
How happy will I be
in such a state
What action I
should do now
Agent
Environment
What the world
is like now
How the world evolves
Agent type
Percepts
Actions
Goals
Environment
(performance measure)
Medical
Symptoms,
diagnonsis system findings, patient’s
answers
Questions, tests,
treatements
Healthy patient,
minimize costs
Patient, hospital
Satellite image
analysis system
Pixels of various
intensity, color
Print a
categorization of
scene
Correct
categorization
Images from orbiting
satellite
Part-picking
robot
Pixels of varying
intensity
Pick up parts and
sort into bins
Place parts in
correct bins
Conveyor belt
with parts
Automated
taxi driver
Cameras,
speedometer, GPS,
sonar, microphone
Steer, accelerate,
brake, talk to
passenger
Safe, fast, legal, Roads, other
comfortable trip, traffic,
maximize profits pedestrians,
customers
Effectors
When there are conflicting goals that cannot all be achieved,
then utility allows an agent to evaluate tradeoffs
Type of environments
●
Accessible vs. inaccessible
Chess vs. poker
●
Deterministic vs. nondeterministic
Poker vs. backgammon
●
Episodic vs. nonepisodic
Package delivery vs. product assembly
●
Characterize these environments
Environment
Accessible
Deterministic
Episodic
Static
Discrete
Chess
Poker
Backgammon
Taxi driving
Medical diagnosis
Static vs. dynamic
Navigation in storage area vs. supermarket
●
Discrete vs. continuous
The real world is inaccessible, stochastic,
sequential, dynamic and continuous
Computer diagnosis vs. medical diagnosis
Some agents we will study this semester
●
– a type of goal-based agent that uses search to find a
sequence of actions that will achieve a goal state
●
Assignment for next class
Problem-solving agent (pp. 55-7)
●
Knowledge-based agent (pp. 151-3)
– Use the internet (or books or magazines) to
find out about such an agent
– performs reasoning based on knowledge that is
explicitly represented in a logical language
●
Planning agent (pp. 337-41)
– similar to a problem-solving agent, but uses more
sophisticated methods of representation and search
●
Learning agent (pp. 525-9)
●
The book discusses many other kinds of agents
– can adapt to a new or changing environment
Find an example of a rational agent (that
uses artificial intelligence)
Characterize the agent’s percepts, actions,
goals, and environment
● Be prepared to briefly describe this AI agent
in class
●