The second part of the course
Introduction to AI
TDDC65 Artificial intelligence and Lisp
Peter Dalenius
petda@ida.liu.se
Department of Computer and Information Science
Linköping University
What is intelligence?
Intelligence is a very general mental capability that,
among other things, involves the ability to
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–
–
–
–
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reason,
plan,
solve problems,
think abstractly,
comprehend complex ideas,
learn quickly and learn from experience.
Using
the functional Lisp programming
language, we will continue to study
some basic concepts and techniques of
artificial intelligence.
A series of lectures and laboratory
exercises.
Russell & Norvig (2003) Artificial
Intelligence: A Modern Approach
Final written exam December 20, 2008.
What is intelligence?
It
is only a word that people use to
name those unknown processes with
which our brains solve problems we call
hard.
It is not merely book learning, a narrow academic
skill, or test-taking smarts. Rather, it reflects a
broader and deeper capability for comprehending our
surroundings – “catching on”, “making sense” of
things, or “figuring out” what to do.
Gottfredson, Linda (1997) Mainstream Science on Intelligence. Intelligence 24:1
Quote attributed to Marvin Minsky, one of the first researchers in AI.
What is artificial intelligence?
Different approaches to AI
Artifical
Which concept
is most important?
intelligence tries to understand
how we think in order to build intelligent
entities (e.g. machines or computer
programs).
AI is a new science, only 50 years old,
but is heavily influenced by other fields.
What do we measure
success against?
Humans
Rationality
Systems that
Systems that
Thought
think
like
humans
think rationally
processes
Behaviour
Systems that act
like humans
Systems that act
rationally
1
1. Systems that think like humans
2. Systems that act like humans
But how do humans think?!
Computer models
from artificial intelligens
Can machines
think?
Experimental methods
from psychology
Cognitive science
Can machines pass
a behavioral
intelligence test?
The Turing test
Trying to build models and theories
about how humans perceive and think.
Turing, Alan (1950) Computing Machinery and Intelligence
The Turing test (one variety)
Part 1: Two persons.
A
B
?
Part 2: A is replaced
by a computer program.
A
B
?
Skills needed to pass the test
Natural
language processing
Knowledge representation
Automated reasoning
Machine learning
Goal: Try to guess which one is male/female.
A helps the interrogator to make the wrong
decision, B helps making the right decision.
3. Systems that think rationally
Aristotle’s laws of thought: Syllogisms
Formalized patterns of reasoning: Logic
Any solveable problem expressed in logic can
be solved!
Problems with this approach:
– Difficult to express informal knowledge using logic
– Computational explosion when trying to draw
conclusions from a large knowledge base
We will look at some of these in the course…
4. Systems that act rationally
Rational agents
– Something or someone that acts (a machine or a
computer program)
– Autonomous, perceiving the environment,
persisting over time, adapting to change, pursuing
goals
Advantages of this approach:
– More general than logic approach. There is more
to rationality than just correct reasoning.
– A completely rational agent has all the skills
needed to pass a Turing test.
– Easier to develop systems when you can define a
degree of rationality for your specific project.
2
Influence from other disciplines
An agent perceives its environment through sensors and
execute actions through its actuators. The input at any given
time is called
percept, and the
complete history
of all percepts is
called a percept
sequence. The agent
is controlled by the
agent program,
which implements
a function from
input to output.
Philosophy
Mathematics
Economics
Neuroscience
Psychology
Computer engineering
Control theory and cybernetics
Linguistics
Intelligent agents
Agent
Intelligent agents
What is rational?
Environment
Performance
Sensors
measure
– Predefined way of telling how good I am.
Percepts
Prior
knowledge of the environment
– What do I know about the world?
Agent
program
Set
of possible actions
– What can I do?
Actuators
Percept
Actions
sequence
– What has happened so far?
Specifying the task environment
Properties of task environments
Agent
Performance
measure
Environment
Actuators
Sensors
Fully
Taxi driver
Safe, fast,
legal,
comfortable
Roads, other
traffic,
customers
Steering,
accelerator,
brake, signal
Camera,
speedometer,
GPS
Part-picking
robot
Percentage of
parts in correct
bin
Conveyor belt
with parts, bins
Jointed arm
and hand
Camera, joint
angle sensors
Interactive
tutor
Maximize
Set of
student’s score students,
on test
testing agency
Display
exercises,
suggestions
Keyboard
entry
or partially observable
Deterministic or stochastic
Episodic or sequential
Static or dynamic
Discrete or continuous
Single agent or multiagent
PEAS = Performance, Environment, Actuators, Sensors
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Agent architectures
Simple reflex agent
Agent
Simple
Sensors
What the world
is like now
Environment
reflex agent
Model-based reflex agent
Goal-based agent
Utility-based agent
Learning agents
What action I
should do now
Condition-action rules
Actuators
Model-based reflex agent
Agent
Goal-based agent
Agent
Sensors
Sensors
State
State
What my actions do
Condition-action rules
What the world
is like now
How the world evolves
What my actions do
What action I
should do now
What it will be like
if i do action A
Goals
Actuators
Learning agent
Agent
Sensors
Sensors
What it will be like
if i do action A
How happy I will be
in such a state
What action I
should do now
Actuators
feedback
changes
Learning
element
Performance
element
knowledge
learning
goals
Environment
Utility
What the world
is like now
Environment
What my actions do
Performance standard
Critic
State
How the world evolves
What action I
should do now
Actuators
Utility-based agent
Agent
Environment
What the world
is like now
Environment
How the world evolves
Problem
generator
Actuators
4
WITAS UAV Project
Architecture
5