1. Artificial Intelligence: INTRODUCTION
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Artificial Intelligence: INTRODUCTION

2. Artificial Intelligence
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Short presentation
Dr. Abdullah Alsheddy
د. عبدالله عبدالعزيز الشدي
Office: FR64
Textbook:
S. Russell and P. Norvig Artificial Intelligence: A Modern Approach, Prentice Hall, 3rd Edition, 2009
Grading:
Quizzes/Presentation/Participation (20%)
Project (20%)
Midterm test (20%)
Final Exam: 40%
Artificial Intelligence : Introduction
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3. Artificial Intelligence
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Course overview
Introduction and Agents (chapters 1,2)
Search (chapters 3,4,5,6)
Logic (chapters 7,8,9)
Planning (chapters 11,12)
Uncertainty (chapters 13,14)
Learning (chapters 18,20)
Robotics (chapter 25,26)
Introduzione all’I.A.: definizione di intelligenza, adattività. Tecniche di ricerca: ricerca su grafi, minimax, A*. Rappresentazione della conoscenza: introduzione alla logica matematica, sistemi a frame, reti di conoscenza, completezza e decidibilità, RDF e OWL. Dimostrazione automatica di teoremi: programmazione logica, risoluzione SLD, inferenze non monotoniche e logiche modali, introduzione al linguaggio Prolog. Situation calculus. Introduzione ai sistemi esperti: forward e backward reasoning, l’algoritmo Rete, il sistema JESS. Problematiche di pianificazione. Introduzione ai sistemi subsimbolici: algoritmi genetici, reti neuronali artificiali, a-life. Sistemi di agenti. Usi industriali dell’I.A.
Artificial Intelligence : Introduction
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4. Artificial Intelligence
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Chapter1 : Outline
What is AI
A brief history
The state of the art
Artificial Intelligence : Introduction
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5. Artificial Intelligence
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What is AI?
Intelligence:
“the capacity to learn and solve problems” (Websters dictionary)
in particular,
the ability to solve novel problems
the ability to act rationally
the ability to act like humans
Artificial Intelligence
build and understand intelligent entities or agents
2 main approaches: “engineering” versus “cognitive modeling”
Artificial Intelligence : Introduction
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6. Intelligent behavior Humans Computer
Artificial Intelligence : Introduction
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7. Why AI?
Cognitive Science: As a way to understand how natural minds and mental phenomena work
e.g., visual perception, memory, learning, language, etc.
Philosophy: As a way to explore some basic and interesting (and important) philosophical questions
e.g., the mind body problem, what is consciousness, etc.
Engineering: To get machines to do a wider variety of useful things
e.g., understand spoken natural language, recognize individual people in visual scenes, find the best travel plan for your vacation, etc.
Artificial Intelligence : Introduction
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8. Artificial Intelligence
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Weak vs. Strong AI
Weak AI: Machines can be made to behave as if they were intelligent
Strong AI: Machines can have consciousness
Subject of fierce debate among philosophers and AI researchers.
E.g. Red Herring article and responses
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9. AI Characterizations
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10. AI Characterizations
Discipline that systematizes and automates intellectual tasks to create machines that :
Act like humans
System passing the Turing Test (1950) Learning from Knowledge (adapt) Representing Knowledge (memorize) Solve Pb (argue) Understanding (communicate)
Theoretical
Act rationally
Rational agent (199X) acts according to his beliefs to reach goals (not only logical)
Pragmatic
Think like humans
Cognitive modeling
(GPS (Newel & Simon,61))
Complex
Think rationally
logical thinking Pascal [ ] (calculating machine) Leibniz [ ] (reasoning machine) Babbage [ ] (Analytical Engine)
limited
Artificial Intelligence : Introduction
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11. Systems that act like humans
Artificial Intelligence
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Systems that act like humans
When does a system behave intelligently?
Turing (1950) Computing Machinery and Intelligence
"Can machines think?"  "Can machines behave intelligently?"
Operational test of intelligence: imitation games
Test requires the collaboration of major components of AI: knowledge, reasoning, language understanding, learning, …
Interrogator interacts with a computer and
a person.
Computer passes the Turing test if
interrogator cannot determine which is
which.
Artificial Intelligence : Introduction
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12. Systems that act like humans
AI is the art of creating machines that perform functions that require intelligence when performed by humans
Methodology: Take an intellectual task at which people are better and make a computer do it
Turing test
Prove a theorem
Play chess
Plan a surgical operation
Diagnose a disease
Navigate in a building
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13. Systems that think like humans
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Systems that think like humans
How do humans think?
Requires scientific theories of internal brain activities (cognitive model):
How to validate? requires :
Predicting and testing human behavior
Identification from neurological data
Brain imaging in action
Cognitive Science vs. Cognitive neuroscience vs. Neuroimaging
They are now distinct from AI
Share that the available theories do not explain anything resembling human intelligence.
Three fields share a principal direction.
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14. Systems that think rationally
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Systems that think rationally
Capturing the laws of thought
Aristotle: What are ‘correct’ argument and thought processes?
Correctness depends on irrefutability of reasoning processes.
This study initiated the field of logic.
The logicist tradition in AI hopes to create intelligent systems using logic programming.
Problems:
Not all intelligence is expressed by logic behavior
What is the purpose of thinking? What thought should one have?
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15. Systems that act rationally
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Systems that act rationally
Rational behavior: “doing the right thing”
The “Right thing” is that what is expected to maximize goal achievement given the available information.
Can include thinking, yet in service of rational action.
Action without thinking: e.g. reflexes.
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16. Systems that act rationally
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Systems that act rationally
Two advantages over previous approaches:
More general than law of thoughts approach
More amenable to scientific development.
Yet rationality is only applicable in ideal environments.
Moreover rationality is not a very good model of reality.
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17. Artificial Intelligence
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Think/Act Rationally
Always make the best decision given what is available (knowledge, time, resources)
Perfect knowledge, unlimited resources  logical reasoning
Imperfect knowledge, limited resources  (limited) rationality
Connection to economics, operational research, and control theory
But ignores role of consciousness, emotions, fear of dying on intelligence
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18. Artificial Intelligence
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Rational agents
An agent is an entity that perceives and acts
This course is about designing rational agents
An agent is a function from percept histories to actions:
For any given class of environments and task we seek the agent (or class of agents) with the best performance.
Problem: computational limitations make perfect rationality unachievable.
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19. Artificial Intelligence
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Foundations of AI
Different fields have contributed to AI in the form of ideas, view points and techniques.
Philosophy: Logic, reasoning, mind as a physical system, foundations of learning, language and rationality.
Mathematics: Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability.
Psychology: adaptation, phenomena of perception and motor control.
Economics: formal theory of rational decisions, game theory.
Linguistics: knowledge representation, grammar.
Neuroscience: physical substrate for mental activities.
Control theory: homeostatic systems, stability, optimal agent design.
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20. Artificial Intelligence
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A brief history
What happened after WWII?
1943: Warren Mc Culloch and Walter Pitts: a model of artificial boolean neurons to perform computations.
First steps toward connectionist computation and learning (Hebbian learning).
Marvin Minsky and Dann Edmonds (1951) constructed the first neural network computer
1950: Alan Turing’s “Computing Machinery and Intelligence”
First complete vision of AI.
Idea of Genetic Algorithms
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21. Artificial Intelligence
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A brief history (2)
The birth of (the term) AI (1956)
Darmouth Workshop bringing together top minds on automata theory, neural nets and the study of intelligence.
Allen Newell and Herbert Simon: The logic theorist (first non-numerical thinking program used for theorem proving).
For the next 20 years the field was dominated by these participants.
Great expectations ( )
Newell and Simon introduced the General Problem Solver.
Imitation of human problem-solving
Arthur Samuel (1952-) investigated game playing (checkers ) with great success.
John McCarthy(1958-) :
Inventor of Lisp (second-oldest high-level language)
Logic oriented, Advice Taker (separation between knowledge and reasoning)
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22. Artificial Intelligence
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A brief history (3)
The birth of AI (1956)
Great expectations continued ..
Marvin Minsky (1958 -)
Introduction of microworlds that appear to require intelligence to solve: e.g. blocks-world.
Anti-logic orientation, society of the mind.
Herbert Gelernter (1959) : constructed the geometry theorem Prover.
Artur Samual ( ) : a series of programs for checkers.
Collapse in AI research ( )
Progress was slower than expected.
Unrealistic predictions.
Some systems lacked scalability.
Combinatorial explosion in search.
Fundamental limitations on techniques and representations.
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23. Artificial Intelligence
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A brief history (4)
AI revival through knowledge-based systems ( )
General-purpose vs. domain specific
E.g. the DENDRAL chemistry project (Buchanan et al. 1969)
First successful knowledge intensive system.
Expert systems
MYCIN to diagnose blood infections (Feigenbaum et al.)
Introduction of uncertainty in reasoning.
Increase in knowledge representation research.
Logic, frames, semantic nets, …
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24. Artificial Intelligence
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A brief history (5)
AI becomes an industry ( present)
First successful commercial expert system R1 at DEC (McDermott, 1982)
Fifth generation project in Japan (1981) : a 10-year plan to build intelligent computer running Prolog.
American response …: US formed the microelectronics and Computer Technology Corporation designed to assure national competitiveness (chip design and human-interface research)
Puts an end to the AI winter.
AI industry boomed from a few million to billion dollars in Period called “AI winter” in which many companies suffered as they failed to deliver on extravagant promises.
Connectionist revival ( present)
Parallel distributed processing (RumelHart and McClelland, 1986); back-propagation learning (computer science and psychology).
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25. Artificial Intelligence
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A brief history (6)
AI becomes a science ( present)
In speech recognition: hidden markov models
In neural networks
In uncertain reasoning and expert systems: Bayesian network formalism
Problem solving …
The emergence of intelligent agents ( present)
The whole agent problem:
“How does an agent act/behave embedded in real environments with continuous sensory inputs”
“Ideally, an intelligent agent takes the best possible action in a situation : study the problem of building intelligent agents in this sense”.
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26. State of the art : AI today 1/2
Autonomous planning and scheduling : on-board autonomous planning program to control the scheduling of operations for a spacecraft (Jonhson et al., 2000).
Game playing : IBM’s Deep Blue became the first computer program to defeat the world champion in chess match (Goodman and Keene, 1997),
Autonomous control : the ALVINN computer vision system was trained to steer a car to keep it following a lane (for 2850 miles ALVINN was in control of steering in 98%, only 2% for human control mostly at exit ramps).
Diagnosis : medical diagnosis programs based on probabilistic analysis have been able to perform at level of an expert physician in several areas in medicine (Heckerman 1991).
Robot driving: DARPA grand challenge
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27. Stanley RobotStanford Racing Team www.stanfordracing.org
Artificial Intelligence : Introduction
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28. Major research areas (Applications)
Artificial Intelligence
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Major research areas (Applications)
Natural Language Understanding
Image, Speech and pattern recognition
Uncertainty Modeling
Problem solving
Knowledge representation
…..
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29. AI Success Story : Medical expert systems
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AI Success Story : Medical expert systems
Programs listed by Special Field
Gynecology
Imaging Analysis
Internal Medicine
Intensive Care
Laboratory Systems
Orthopedics
Pediatrics
Pulmonology & Ventilation
Surgery & Post-Operative Care
Trauma Management
Antibiotics & Infectious Diseases
Cancer
Chest pain
Dentistry
Dermatology
Drugs & Toxicology
Emergency
Epilepsy
Family Practice
Genetics
Geriatrics
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30. Pattern Recognition Applications
Artificial Intelligence
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Pattern Recognition Applications
Handwriting and document recognition
Signature, biometrics (finger, face, iris, etc.)
Trafic monitoring, Remote Sensing
guided missile, target homing
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31. Artificial Intelligence
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Future of AI
Making AI Easy to use
Easy-to-use Expert system building tools
Web auto translation system
Recognition-based Interface Packages
Integrated Paradigm
Symbolic Processing + Neural Processing
AI in everywhere, AI in nowhere
AI embedded in all products
Ubiquitous Computing, Pervasive Computing
Artificial Intelligence : Introduction
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32. Quiz Does a plane fly? Does a boat swim? Does a computer think?
Artificial Intelligence : Introduction
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