Artificial Intelligence CAP492

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Artificial Intelligence CAP492

• Dr. Souham Meshoul
• Information Technology Department
• CCIS King Saud University
• Riyadh, Saudi Arabia
• meshoul_at_ccis.ksu.edu.sa

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE


Chapter 1
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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Goal of Artificial Intelligence Not only to
  understand how does mind work? but also how to
  build intelligent entities?.
• Engineering point of view -Solve real-world
  problems using knowledge and reasoning
• -Develop concepts,
  theory and practice of building intelligent
  entities
• - Emphasis on system
  building
• Scientific point of view - Use
  computers as a platform for studying intelligence
  itself
• 
  - Emphasis on understanding intelligent behavior.
• Artificial Intelligence is one of the newest
  sciences which emerged after the world war II. AI
  represents a big and open field.
• The name Artificial Intelligence was adopted for
  the first time in 1956. (Computational
  Intelligence)
• Artificial Intelligence can be viewed as a
  universal field Ho to automate intellectual
  tasks?

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• What is artificial Intelligence?
• Several definitions are available in the
  literature.
• Thinking vs
  Behavior
• Model humans vs Work
  from an ideal standard
• Two points of views
• 1. Thinking/Acting humanly success is
  measured in term of fidelity to human
  performance.
• 2. Thinking/Acting rationally success
  is measured using an ideal concept of
  intelligence called
• Rationality.
• Rational System system which does the right
  thing given what it knows.

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Some AI Definitions
• According to thought processes and reasoning
• Thinking like humans
• The exciting new effort to make computers
  thinkmachines with minds, in the full and
  literal sense. (Haugeland, 1985).
• The automation of activities that we associate
  with human thinking, activities such as
  decision-making, problem solving, learning
  (bellman, 1978).
• Thinking rationally
• The study of mental faculties through the use
  of computational models. (Charniak and
  Mcdermott, 1985).
• The study of the computations that make it
  possible to perceive, reason and act. (Winston
  1992).

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Some AI Definitions
• According to behavior
• Acting Humanly
• The art of creating machines that perform
  functions that require intelligence when
  performed by people. (Kurzweil, 1990)
• The study of how to make computers do things at
  which, at the moment, people are better. (Rich
  and Knight, 1991)
• Acting rationally
• Computational intelligence is the study of the
  design of intelligent agents. (Poole et
  al.,1998).
• AIis concerned with intelligent behavior in
  artifacts. (Nilsson, 1998).

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Some other definitions
• Weak AI (Searle) AI develops useful, powerful
  applications
• Strong AI claims machines have cognitive minds
  comparable to humans
• debates on strong AI bring up some difficult
  conceptual problems in philosophy..
• Keep this one Science of making machines do
  things that would require intelligence if done by
  humans Marvin Minsky

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Acting Humanly
• Turing Test Alan Turing 1950 Computing
  Machinery and Intelligence

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Test The computer passes the test of
  intelligence if a human, after posing some
  written questions, cannot tell whether the
  responses were from a person or not.
• Provides an operational definition of AI.
• Still being used today within the AI community in
  annual competitions the Loebner Prize (100,000
  and a true 18K gold medal)
• To give an answer, the computer would need to
  posses some capabilities
• Natural language processing To communicate
  successfully.
• Knowledge representation To store what it knows
  or hears.
• Automated reasoning to answer questions and draw
  conclusions using stored information.
• Machine learning To adapt to new circumstances
  and to detect and extrapolate patterns.
• Computer vision To perceive objects.
• Robotics to manipulate objects and move.

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Example ELIZA program, some extracts from the
  test
• Patient Men are all alike.
• ELIZA In what way?
• Patient They are always bugging about something
  or the other.
• ELIZA Can you think of a specific example?
• Patient Well, my boyfriend made me come here.
• ELIZA Your boyfriend made you come here?
• Patient He says Im depressed much of the time.
• ELIZA I am sorry you are depressed.
• Problems
• ELIZA and similar programs stressed simple
  syntactic analysis and generation of sentences.
  They used pattern matching with known sentences.
• Turings test is not reproducible or amenable to
  mathematical analysis (it suggests duplicate an
  examplar).
• An alternative Study the underlying principles
  of intelligence (Wright brothers)

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Thinking Humanly
• Program think like human ?
  How humans think?
• Requires Scientific theories of internal
  activities of the brain (cognitive science and
  cognitive neuroscience).
• Example
• The General Problem Solver (GPS designed by
  Newell and Simon In 1963) was meant to be a
  program that simulated human thought.
• GPS used means-end analysis in its search for
  solutions, computing the difference between the
  goal and current, and then attempting to minimize
  the difference.
• Newell and Simon by comparing GPS traces with
  those of human subjects discovered that the
  behavior of GPS was largely a subset of human
  behavior

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Thinking rationally
• The Laws of Thought approach is based on pattern
  for argument structure arising from Aristostles
  syllogisms.
• Example, Socrates is a man all men are mortal,
  therefore Socrates is mortal. The laws of
  thought initiated the field of logic.
• The formal logic movement was advanced by Peano,
  Boole, Frege,, Godell and others (late 1800s
  and early 1900s)
• Inspired perhaps by early progress, Hibert became
  a proponent of a school of thought known as
  logicism or formalism. The goal of this was to
  devise a logic, or formal system, capable of
  deriving all mathematical theorems.

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Acting rationally
• Modern AI can be characterized as the engineering
  of rational agents.
• An agent is simply an entity that perceives and
  acts. A rational agent is an entity that
  perceives, reasons and acts rationally
  (correctly).

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Foundations
• An interdisciplinary subject found on
• Philosophy,
• mathematics,
• economics,
• neuroscience,
• psychology,
• computer engineering,
• linguistics, and so on

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• History of Artificial Intelligence
• Big dream
• Ultimately, we are dealing with the question
  What are we (human beings) doing when we are
  thinking?
• Thought processes in the human mind are
  computational in nature. There are mechanistic
  procedures for generating these thoughts.
• Such computations can be simulated and
  implemented by a Turing machine. Therefore, it
  can be programmed.

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• History of Artificial Intelligence
• Early days (1943-1955)
• 1943 first piece of AI work Warren McCulloch
  and Walter Pitts
• Model of artificial neurons
• Mathematical learnable functions that generate
  on/off depending on inputs (logic gates)
• Any computable function can be computed by a
  network of connected neurons.
• Suitably defined networks can learn.
• 1949 Hebbian learning
• A mechanism for updating the connection strength
  of a neuron.
• Today, neurologists have confirmed that something
  similar to Hebbian learning indeed is going on in
  our brain when we are learning.
• 1950 Turing test, complete vision of AI in
  computing machinery and Intelligence
• 1951 first neural network computer
• Implemented by M. Minsky and D. Edmonds

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• History of Artificial Intelligence
• Early days (1943-1955) Mcculloch and pitts
  artificial neuron

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• History of Artificial Intelligence
• Birth of AI 1956
• 1956 Dartmouth Conference
• Organized by John McCarthy and colleagues for
  starting a new area in studying computation and
  intelligence.
• John McCarthy introduced the term artificial
  intelligence in the conference.
• The next 20 years witnessed steady growth of the
  field led by the pioneers appeared in the
  Dartmouth conference.

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• History of Artificial Intelligence
• Expectations and Initial enthusiasm (1952 1969)
• 1956 Samuels checkers program
• First game playing program achieving
  human-competitive performance.
• 1957 Simons general problem solver (GPS)
• Imitates the way a human would solve planning
  problems.
• 1958 Invention of LISP by J. McCarthy.
• The first AI programming language.
• 1958 Minskys microworlds
• The concept of creating a controlled
  environment in which problem solving appears to
  require intelligence was born. The study of
  computation and intelligence can become more
  manageable in these micro-worlds

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Expectations and Initial enthusiasm (1952 1969)
• 1963 Thomas Evans program ANALOG
• Solved analogy problems in an IQ test.
• 1965 ELIZA
• Simulates a dialog with a computer in English on
  any topic.
• Became popular when programmed to simulate a
  psychotherapist (Fedoras Emacs).
• 1967 Dendral program (developed at Stanford)
• First successful program for scientific reasoning
  one of the earlier rule based expert systems. A
  program that can infer molecular structures given
  the information provided by a mass spectrometer
  (that gives the masses of the various fragments
  of a molecule). The program relies on expert
  knowledge (encoded as rules) to constraint the
  generation of possible molecular structures that
  are consistent with the information from the mass
  spectrometer

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• History of Artificial Intelligence
• Reality Check (1966 1973) series of
  disappointments and frustrations
• AI was poured
  little buckets of reality cold water
• Problems
• Most early systems contain little or no knowledge
  of their subject matter
• Knowledge acquisition bottleneck.
• Example Poor performance of earlier machine
  translation system (Russian ? English) the
  spirit is willing but the flesh is weak was
  translated to the vodka is good but the meat is
  rotten.
• Computational Intractability of AI problems
• Theory of computational complexity was not
  developed. Polynomial solvable problems,
  NP-completeness, etc
• People thought a faster machine could solve any
  hard problem.
• Initial frustration with theorem proving led to a
  disappointment in AI. Theorem proving is
  exponential in complexity

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• History of Artificial Intelligence
• Resurgence (1969 1979)
• 1971 T. Winograds Ph.D. thesis (MIT)
  demonstrated a system that can understand English
  in a micro-domain (the block world).
• 1972 PROLOG was developed by a group of
  Europeans and became alternative to LISP as an AI
  programming language.
• 1974 MYCIN was developed by Ted Shortliffe.
  Expert system for medical diagnosis. Sometimes
  called the first expert system.
• 1978 The Version Space algorithm was developed
  by Tom Mitchell at Stanford. First symbolic
  machine learning algorithm. Father of Machine
  Learning.
• 1979 Non-monotonic logic. Began to be formalized
  by John McCarthy and his colleagues.

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• History of Artificial Intelligence
• Resurgence (1969 1979) Winograd 1972

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• History of Artificial Intelligence
• AI becomes an industry (1980 present)
• AI started to become industrially and
  commercially beneficial
• 1982 R1 was deployed at DEC an expert system
  that saved the company around 40M / year
• Du Pont had 100 in use and an estimated 500 in
  development at late 90s to early 21st century
• At an international level, AI was considered a
  part of a countrys technological developments
• Japan First Generation project (10 year plan
  to build intelligence machines running in Prolog)
• USA Microelectronics and Computer Technology
  Corporation (MCC) was formed in response
• Britain Funding for AI was reinstated

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• History of Artificial Intelligence
• Renewing with connectionism and AI becomes a
  science (1986 present)
• Work of the physicist John Hopfield (1982) on
  using techniques from statistical mechanics.
• Connectionist models of intelligent systems
  competitor to the symbolic models (Newell and
  Simon) and logicist approach (McCarthy).
  (complementary approaches in fact).
• Several revolutions in many fields pattern
  recognition, computer vision, robotics
• Emergence of intelligent agents.

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Examples of AI applications Game Playing

• TDGammon, the world champion backgammon player,
  built by Gerry Tesauro of IBM research.
• Perception keyboard input.
• Reason reinforcement learning.
• Actuation graphical output shows dice and
  movement of piece.
• Deep Blue chess program beat world champion Gary
  Kasparov
• Perception input symptoms and test results.
• Reason Bayesian networks, Monte-Carlo
  simulations.
• Actuation output diagnoses and further test
  suggestions.

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Examples of AI applications Natural Language
  Understanding
• Natural language understanding (spell checkers,
  grammar checkers)
• AI translators spoken to and prints what one
  wants in foreign languages Alta Vistas
  translation of web pages.
• Advanced systems can answer questions based on
  the information in the text and produce useful
  summaries.
• PROVERB (Littman 1999) crossword puzzles
• Examples of successes English conversation
• START system accesses raw data tables, and then
  can carry on a dialogue

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Examples of AI applications Expert systems
• In geology
• prospector expert system carries evaluation of
  mineral potential of geological site or region
• Diagnostic Systems
• Pathfinder, a medical diagnosis system (suggests
  tests and makes diagnosis) developed by Heckerman
  and other Microsoft research
• Microsoft Office Assistant in Office provides
  customized help by decision-theoretic reasoning
  by an individual user.
• MYCIN system for diagnosing bacterial infections
  of the blood and suggesting treatments
• System Configuration
• "XCON" (for custom hardware configuration)
  configures computers doing work of 300 people
  using 10,000 rules

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Examples of AI applications Robotics
• Robotics becoming increasing important in various
  areas like games, to handle hazardous conditions
  and to do tedious jobs among other things.
• Examples automated cars, ping pong player,
  mining, construction, robot assistant in
  microsurgery,

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Main issues in AI
• Representation
• Search many tasks can be viewed as searching a
  very large problem space for solution space
• Inference related to search, inferring other
  facts from some given facts. e.g., knowing all
  elephants have trunks and Jo is an elephant,
  can we answer does Jo have a trunk?
• Learning inductive inference, neural networks,
  artificial life, genetic algorithms, evolutionary
  strategies
• Planning starting with general facts about the
  world, facts about the effects of basic actions,
  facts about a particular situation, and a
  statement of a goal, generate a strategy for
  achieving that goal in terms of a sequence of
  primitive steps or actions

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Summary
• Intelligence is studied from many perspectives
  Are you concerned with thinking or behavior?
• AI can help us solve difficult, real-world
  problems, creating new opportunities in business,
  engineering, and many other application areas.
• The history of AI has had cycles of success,
  misplaced optimism, and resulting cutbacks in
  enthusiasm and funding. There have also been
  cycles of introducing new creative approaches and
  systematically refining the best ones.
• AI has advanced more rapidly in the past decade
  because of greater use of the scientific method
  in experimenting with and comparing approaches.