CSI%204106%20Introduction%20to%20Artificial%20Intelligence%20Winter%202005

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CSI 4106Introduction to Artificial
IntelligenceWinter 2005
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Some Information (1)

• Instructor Dr. Nathalie Japkowicz
• Office STE 5-029
• Phone Number 562-5800 x 6693 (dont rely on it!)
• E-mail nat_at_site.uottawa.ca (best way to contact
  me!)
• Office Hours Monday, Wednesday 100pm-200pm
  
• or by appointment

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Some Information (2)

• Textbook Luger, George, F. Artificial
  Intelligence, Structures and Strategies for
  Complex Problem Solving , Addison Wesley, Fifth
  Edition, 2005.
• Course Requirements
• 3 Assignments. 30
• Project Report/Presentation ..15
• Midterm Exam.20
• Final Exam35

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Assignments

• Assignments must be handed in at the beginning of
  classes, the day they are due. There are no
  make-up assignments. The three assignments will
  have to be handed in on the following days. They
  will be posted two weeks before their due-date.
• Assignment 1 (LISP/Search) -----
• Due Date Wednesday, February 6, 2008
  
• Assignment 2 (PROLOG/Logic) ------
• Due Date Wednesday, March 5, 2008
• Assignment 3 (WEKA/Learning) ------
• Due Date Wednesday, April 2, 2008

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Project

• Students, in teams of two, will do a project on
  the practical applications of Artificial
  Intelligence.
• This will involve carrying out research on the
  topic of the teams choice, submitting a report
  on this research, and giving an in-class
  presentation of 15 or so minutes, during which
  both team members will have to speak.
• You can choose a topic from one of the following
  areas of application

• Computer Games (A very popular topic, in
  general !)
• Expert Systems
• Robotics
• Planning

• Natural Language Processing
• Machine Learning/Data Mining
• Neural Networks
• Genetic Algorithms
• AI and Psychology

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Topics

• Overview
• Knowledge and Search
• Search
• Basic Search Methods
• Heuristic Search
• Games
• Knowledge Representation
• Logic
• Rules
• Uncertainty
• Natural Language Processing
• Basic Facts about English
• Syntax
• Semantics
• Planning
• Machine Learning

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Definitions, overview, history

• Points
• Definitions of AIsystems that
• think like humans
• act like humans
• think rationally
• act rationally
• Physical-symbol systems
• Sources and areas of AI
• Bits of history

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Definitions of Artificial Intelligence

• A general classification of AI systems, due to
  Russell and Norvig (1995, 2003)

systems thatthink like humans systems thatthink rationally
systems thatact like humans systems thatact rationally
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The Turing test

• Assessing intelligence by observation is biased
  the experimenter is guided by guesses rather than
  measurable properties. This is a blind test.

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Systems that think like humans

• AI systems of this type would try to recreate the
  human mind and its innate (precoded?) cognition
  mechanisms.
• This is very difficult, because it requires a
  thorough understanding of psychology,
  neurophysiology, and philosophy.
• Such systems would belong to Cognitive Science
  rather than Artificial Intelligence.

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Systems that act like humans

• E. Rich K. Knight (1991)
• AI is the study of how to make computers do
  things which, at the moment, people do better

• perception,
• communication,
• reasoning,
• learning,
• planning.

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... act like humans (2)

• We do not even consider social behaviour, sense
  of humour, appreciation of arts and other talents
  that so far only Science Fiction gives to
  machines.
• Even an approximation of these faculties requires
  vast amounts of knowledge (to represent
  explicitly cultural background, common sense and
  so on).
• People also rely on experience -- perhaps on
  memory patterns that we do not yet know how to
  recreate in computer systems.

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... act like humans (3)

• Things at which computers will soon be
  appreciably better advice, diagnosis, fault
  detection, forecasting...
• Those would be systems where specific technical
  knowledge plays a central role.
• Measurable success will come when we solve the
  problems of organizing and acquiring vast
  knowledge.
• We also need experience, rules-of-thumb, and the
  ability to reason in the absence of full
  information.

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... act like humans (4)

• Things at which computers are already better, or
  nearly so
• formalized games such as chess, chequers,
  backgammon, Othello
• formal inference (but not creativity and
  invention).
• They do require good heuristics -- shortcuts --
  of the kind that skilled people apply, sometimes
  even without conscious reflection.

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... act like humans (5)

• Neat it is easy to verify the success of all
  these tasks (after all, we are better). The tasks
  are challenging, and can hardly be solved by
  algorithmic means.
• Ugly the amount of necessary knowledge is
  overwhelming too many tasks end up solved in a
  toy form. Heuristics are fallible, and AI systems
  are not trusted as they perhaps deserve to be.

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Systems that think rationally

• E. Charniak D. McDermott (1985)
• AI is the study of mental faculties through the
  use of computational models.
• Mental faculties (reasoning, learning,
  perception) are studied more or less as in
  psychology, except that working with programs is
  easier and more objective, more measurable.
• On the other hand, programs require full and
  explicitly stated knowledge.

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... think rationally (2)

• Does "computational" imply computing? Do brains
  work like computers? No, but
• what brain does may be thought of as a kind of
  computation.

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... think rationally (3)

• P. H. Winston (1992)
• AI is the study of the computations that make it
  possible to perceive, reason, and act.
• These are the hallmarks of intelligence, and they
  can be measured more or less objectively.
• Now, if we could agree that this is what
  intelligence is about...

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... think rationally (4)

• AI can be indirectly characterized by (some of)
  its goals
• make computers more useful,
• understand the principles that make intelligence
  possible.
• The contribution of AI methods and techniques to
  the classical study of intelligence
• computational metaphors for mental processes,
• precision of the data and structures (that is,
  knowledge),
• establishing practical limits for "intelligent"
  programs,
• repeatability of experiments -- and no ethical
  problems.

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Systems that act rationally

• G. F. Luger W. F. Stubblefield (1993),G. F.
  Luger (2005)
• AI is the branch of computer science concerned
  with the automation of intelligent behaviour.
• This means seeing AI as part of computer science
  that grows out of the same basic principles.

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...act rationally (2)

• Once more, we ask what is intelligence (if it can
  be defined, so can AI)
• is intelligence innate or acquired?
• what is the essence of learning, creativity,
  intuition?
• can we observe intelligence without knowing the
  internal mechanisms (memory, search)?
• can psychology, neurology and other related
  fields help build AI systems? is it possible to
  have intelligence without a host (body)?
• These questions show how much is yet unknown.
  Practical AI (building systems in the absence of
  a philosophical foundation) is more like a blind
  search for answers.

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Physical-symbol systems

• A physical-symbol system is collection of
• expressions built of elementary symbols (without
  inherent meaning), and
• processes that create and modify such expressions
• that exist in the context of the "real world".
  Symbols can be mapped into real-world entities,
  and processes into real-world events.
• A physical-symbol system is what we may call a
  model of the real world.

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Physical-symbol systems (2)

• The physical-symbol system hypothesiswe can
  model intelligence.
• M. Ginsberg (1993)
• AI is the enterprise of constructing a
  physical-symbol system that can reliably pass the
  Turing test.
• G. F. Luger (2005) revised definition
• AI is the study of the mechanisms underlying
  intelligent behaviour through the construction
  and evaluation of artifacts designed to enact
  those mechanisms.

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The sources of Artificial Intelligence

• Philosophy (ontology, epistemology, ...)
• Mathematics (logic, geometry, probability,
  decision theory, ...)
• Psychology
• Linguistics, psycholinguistics
• Computing (theory engineering practice)

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The areas of Artificial Intelligence

• Search (blind, informed, adversarial)
• Knowledge representation (logic, semantic
  networks, frames, rules, neural networks)
• Planning
• Machine Learning (symbolic, statistical data
  mining)
• Natural Language Processing (symbolic,
  statistical text mining)
• Perception (vision, speech)
• Robotics

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Elements of the history of Artificial Intelligence

• The forerunners of AI
• information theory,
• cybernetics (the study of communication and
  control processes in biological, mechanical, and
  electronic systems comparison of these processes
  in biological and artificial systems).
• Simple neural network computers (!) were also
  built in 1940s and early 1950s.

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... history of AI (2)

• The first, very ambitious, tasks that computing
  science set itself included Machine Translation
  and Chess Playing (Shannon 1950). Artificial
  Intelligence was not in the cards yet...
• These have not been too successful machine
  translation is still more craft than science, and
  computer chess has only recently become truly
  competitive, thanks to specialized or superfast
  hardware.

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... history of AI (3)

• The term "Artificial Intelligence" has been
  coined in mid-1950s by John McCarthy (later the
  inventor of Lisp).
• The first period of growth -- and funding -- came
  in the 1960s. General Problem Solver (Newell
  Simon 1972) Aristotelian (!) means-ends
  analysis.
• Other early applications analogy discovery
  simple question-answering systems in toy domains.

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... history of AI (4)

• There followed a disillusionment and the
  withdrawal of funds.
• Renewed interest in the late 1970s brought large
  funding (particularly from the military). In this
  period more and more subtle knowledge
  representation methods, first of all standard
  logic and various advanced logics.
• AI is sometimes seen as "applied logic" (Nilsson,
  early 1970s).

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... history of AI (5)

• Programming languages best suited to AI tasks are
  Lisp (1960) and Prolog (1972). There also have
  been specialized knowledge representation systems
  and languages, used to develop knowledge bases
  and knowledge-based systems. This includes expert
  systems, in which probability and beliefs play an
  important role. Commercialization of some expert
  systems is one the signs of the growing maturity
  of AI.

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... history of AI (6)

• First textbooks appeared late (1971, then 1984).
  No theory of AI exists in spite of the massive
  publication rate and the bandwagon effect
  (Genesereth Nilsson 1987 is a rare textbook
  devoted to the foundations of AI).
• Fads and trends expert systems, genetic
  algorithms, neural networks, data mining.
  Successes have been rare and sometimes bizarre
  are intelligent warheads a success?

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Thats it.

• We will now turn to methods, tools and techniques
  (but we will occasionally look at a bit of
  theory).