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ARTIFICIAL INTELLIGENCE
CIS 430 / 530
INTRODUCTION
A definition of Artificial Intelligence:
1. The study of the computations that make it possible to
perceive, reason and act.
2. The study of how to make computers do things which at the
moment people do better.
3. The study of problems requiring large amounts of
knowledge.
4. The study of techniques to solve exponentially hard
problems in polynomial time.
5. The study of solving hard problems.
6. Study of agents that exist in an environment and perceive
and act. **
CATEGORIES OF AI DEFINITIONS
Thinks like a human | Thinks rationally
------------------------ |-----------------------|
Acts like a human
| Acts rationally**
1. Thinks like a human: Cognitive science uses AI and psychology
to develop and test theories of human thinking;
2. Acts like a human: Turing test
Requires
- Natural Language Processing
- Knowledge representation
- Automated reasoning: Draw new conclusions
- Machine Learning: Detect patterns
- Computer Vision
- Robotics
Does not address the underlying principles of intelligence only
behavior.
3. Thinks rationally: Must express everything logically --> chapter 7
4. Act rationally: Given beliefs (knowledge) --> act so as to achieve
goals;
Easier to study behavior;
All behavior seems rational to the behavior at the time of
the behaving;
How to construct rational agents:
Intelligence Î Rationality {Does the right thing}
GOALS OF ARTIFICIAL INTELLIGENCE
- TO BUILD AN INTELLIGENT MACHINE
- TO FIND OUT ABOUT THE NATURE OF
INTELLIGENCE {THE COMPUTER AS A TOOL
FOR TESTING THEORIES OF INTELLGENCE
ARTIFICIAL INTELLIGENCE WAS A TERM COINED BY JOHN
McCARTHY IN 1956. JOHN McCARTHY WAS ALSO THE
INVENTOR OF THE PROGRAMMING LANGUAGE LISP.
** AT THE PRESENT TIME, AI PROGRAMS DON’T KNOW
WHAT THEY KNOW , NOR ARE THEY AWARE OF WHAT THEY
CAN DO.
FOUNDATIONS
(History of AI)
PHILOSOPHY (428 B.C. --)
What is the difference between
man and animal?
- soul
- ability to think
(to advance society)
- Inductive thinking
{Specific to General}
MATHEMATICS (800 --)
- Gives us the limits of computation;
- Algorithm
- Logic (Boolean logic -- *1815 -1864*)
- Incompleteness theoremÆ There are true
statements whose truth cannot be
established with an algorithm.
- Intractable problem --> Grows
exponentially with the size of the
problem.
- NP completenessÆ Used to identify
intractable problems.
- Probability theory (Bayes’ rule) for
dealing with an uncertain world.
- Decision TheoryÆ
ECONOMICS (1776 – PRESENT)
Utility Æ The mathematics of preferred outcomes.
Decision Theory Æ Combines probability theory and
utility theory to make decisions under uncertain
conditions.
NEUROSCIENCE (1861- )
- Study of the nervous system (the brain).
PSYCHOLOGY
- Wilhelm Wundt (1832-1920) Father of Psychology
- Mind contains and processes information
- Cognitive Psychology
COMPUTER ENGINEERING
- Computer as an artifact to be used for the study of
intelligence.
- How do we build an efficient computer?
CONTROL THEORY AND CYBERNETICS (1948 - )
- How do artifacts operate independently?
- Cybernetics: The theoretical study of communication
and control processes in biological, mechanical and
electronic systems, especially the comparison of these
processes in biological and artificial systems.
LINGUISTICS
- Knowledge representation -> How to place knowledge
into a computer so as to all for reasoning.
- Natural Language processing
HISTORY OF ARTIFICIAL INTELLIGENCE
- FIRST MODEL OF A NEURON (1943) Æ McCulloch and
Pitts
- FIRST NEURAL NETWORK (1951) Æ Minsky and Edmonds
- 1956 DARTMOUTH COLLEGE Æ John McCarthy coined the
term Artificial Intelligence.
- LOGIC THEORIST (Theorem prover) (1956) Æ Simon and
Newell
- MICROWORLD APPLICATIONS Æ Game playing
- REAL-WORLD APPLICATIONS Æ Problems with
computational complexity.
- KNOWLEDGE-BASED SYSTEMS (1969-79)
- AI IN INDUSTRY (1980 -)
RECENT WORK
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Neural Networks
Genetic Algorithms
Data Mining
Natural language Processing
Vision and Speech
Robotics
Expert Systems
Intelligent Database Retrieval
Combinatorial and Scheduling Problems
Intelligent Tutoring Systems
Data Warehousing
Intelligent Agents
PHYSICAL SYMBOL SYSTEM HYPOTHESIS
A PHYSICAL SYMBOL SYSTEM HAS THE NECESSARY AND
SUFFICIENT MEANS FOR GENERAL INTELLIGENT BEHAVIOR.
A PHYSICAL SYMBOL SYSTEM CONTAINS THE FOLLOWING:
1. A SET OF SYMBOLS
2. A SET OF SYMBOL STRUCTURES OR EXPRESSIONS
3. PROCESSES OPERATING ON EXPRESSIONS CREATING
NEW EXPRESSIONS.
AN EXAMPLE
BARKS(X) & TAIL(X) Æ DOG(X)
BARKS(FIDO)
TAIL (FIDO)
WE CAN CONCLUDE DOG(FIDO)
PRODUCTION SYSTEM
PRODUCTION SYSTEMS ARE FREQUENTLY USED TO
MODEL HUMAN INTELLIGENCE;
MAJOR COMPONENTS INCLUDE:
1. GLOBAL DATABASE OF FACTS
2. SET OF RULES
3. CONTROL STRATEGY
Depth-first Search
Forward Chining
Backward Chaining
Breadth-first Search
Forward Chaining
Backward Chaining
TWO FUNAMENTAL PROBLEM SOLVING ISSUES
1. How to represent knowledge
- Propositional & predicate logic
- semantic networks
- object oriented methods
2. How to reason with the knowledge represented
- Problem solving strategy (search strategy).
- Search involves the systematic exploration of a
state space (problem space) containing the set of
all possible problem states that may exist.
PROBLEM DEFINITION
Problem definition is fundamental to the solution of complex
problems.
Problem definition includes:
1. Defining the state space to be searched.
2. Specifying the initial state(s)
3. Specifying the goal state(s)
4. Specifying the rules that represent actions.
***ALL PROBLEMS CAN BE MODELED AS A STATE
SPACE SEARCH***.
- Simple search is unintelligent. Simple search
must be complemented with heuristics.
- Heuristics are rules of thumb that will usually
limit the search space.
STOCK HEURISTICS
• BUY ON THE RUMOR, SELL ON THE NEWS
• AS INTEREST RATES RISE, INTERNET STOCK
PRICES FALL
• GOOD NEWS FOR 1 STOCK IN A SETOR TENDS TO
MAKE OTHER STOCKS IN THE SECTOR RISE.
• JANUARY IS SMALL CAP MONTH
• WINDOWDRESSING
• SANTA CLAUS RALLY
• BUY A STOCK WHEN IT REACHES A NEW HIGH
• BUY IN NOVEMBER, SELL IN APRIL
FACT
Given 100 individuals at age 65:
• 1 will have wealth in excess of 5 million dollars
• 4 will have wealth in excess of 1 million dollars
• 41 will still be working
• 54 will be dead broke
THE WATER JUG PROBLEM
Action
1. Fill the 4-gallon jug.
2. Fill the 3-gallon jug.
3. Empty the 4-gallon jug
Onto the ground.
4. Empty the 3-gallon jug
Onto the ground.
5. Pour water from the
3-gallon jug into the 4-gallon
jug until the 4-gallon jug is full.
6. Pour water from the 4-gallon
jug into the 3-gallon jug until
the 3-gallon jug is full.
7. Pour all the water from the
3-gallon jug into the 4-gallon jug.
8. Pour all the water from the
4-gallon jug into the 3-gallon jug.
Required
Conditions
Resultant
State
The 4-gallon jug is not full.
The 3-gallon jug is not full.
The 4-gallon jug is not empty.
(4,y)
(x,3)
(0,y)
The 3-gallon jug is not empty.
(x,0)
The total amount of water in both jugs
is > = 4 and the 3-gallon jug is not empty.
The total amount of water in both jugs
is > = 3 and the 4-gallon jug is not empty.
(4,y – (4 – x))
The total amount of water in both jugs
is <=4 and the 3-gallon jug is not empty.
The total amount of water in both jugs
is <=3 and the 4-gallon jug is not empty.
(x – (3 – y),3)
(x + y,0)
(0, x + y)
MISSIONARIES AND CANNIBALS
PROBLEM
Three missionaries and three cannibals find themselves on one side
of a river. They have agreed that they would like to get to the other
side. The missionaries want to arrange the trip across the river so
that the number of missionaries on either side of the river is never
less than the number of cannibals who are on the same side. The
only boat available holds a maximum of two people. Draw the
state space for this problem making sure that the missionaries
never find themselves at risk of being eaten.
RESOURCES
Journals & Magazines:
Communications of the ACM
AI Magazine
Artificial Intelligence
IEEE Computer
IEEE Transactions on Knowledge and Data Engineering
IEEE Intelligent Systems
Pattern Recognition
Data Mining Journal
PC AI
*Many others
Web Sites:
Data mining
www.kdnuggets.com
http://www.cs.waikato.ac.nz/~ml/weka/
Expert System Building Tools
http://www.ghgcorp.com/clips/Download.html
documentation, then usrguide.pdf
executables, pc, clipswin.zip
clipshlp.zip
ES (An expert system for fuzzy logic):
summers.tar
www-2.cs.cmu.edu/Groups/AI/
html/faqs/ai/expert/part1/faq-doc-5.html
Fuzzy Clips
RULE-BASED SYSTEMS
• PRODUCTION RULES
• LOGICAL OPERATORS
• MODUS PONENS
• ABDUCTION
• INDUCTION & DEDUCTION
• ASSOCIATION RULES