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Artificial Intelligence,
Ontologies,
and Common Sense
Ray Larson & Warren Sack
University of California, Berkeley
School of Information Management and Systems
SIMS 202: Information Organization and Retrieval
Lecture author: Warren Sack
30 Oct 2001
IS202: Information Organization and Retrieval
Last Time
• Metadata is:
– “data about data” (database systems)
– Information about Information
– Structures and Languages for the Description of
Information Resources and their elements
(components or features)
– “Metadata is information on the organization of the
data, the various data domains, and the
relationship between them” (Baeza-Yates p. 142)
30 Oct 2001
IS202: Information Organization and Retrieval
Examples of Metadata
• Bibliographic Metadata (traditional
Library cataloging)
• Dublin Core
30 Oct 2001
IS202: Information Organization and Retrieval
Today
• What is Cognitive Science?
• What is Artificial Intelligence?
– Knowledge Representation
• Languages
– Representing Common Sense
• Common Sense Interfaces
• Story Understanding, Story Generation, and
Common Sense
30 Oct 2001
IS202: Information Organization and Retrieval
Cognitive Science/The Next
Four Lectures
• 10/30/01 – AI, knowledge
representation and common sense
• 11/01/01 – Computational Linguistics,
Cognitive Psychology and Lexical
Knowledge
• 11/06/01 – AI and information extraction
• 11/08/01 – Linguistics, Philosophy,
Psychology, categories, and cognition
30 Oct 2001
IS202: Information Organization and Retrieval
What is Cognitive Science?
Definition by “symptoms”
• A definition from Howard Gardner (1986) The
Mind’s New Science; the five “symptoms of
cognitive science”; the first two are central,
the next three are strategic
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(1) mental representations
(2) computers
(3) emphasis
(4) epistemology
(5) interdisciplinarity
30 Oct 2001
IS202: Information Organization and Retrieval
Symptom 1 of Cognitive Science:
Mental Representations
• To study human cognition it is necessary to
posit mental representations and examine
those representations separately from the
“low level” biological or neurological, on one
hand, and also separately from the “high
level” social or cultural, on the other hand.
(adapted from Gardner, 1986)
30 Oct 2001
IS202: Information Organization and Retrieval
Symptom 2 of Cognitive Science:
Computers
• Computers are central to any
understanding of the human mind.
They are essential both as tools, but
also as models of how the mind works.
(adapted from Gardner, 1986)
30 Oct 2001
IS202: Information Organization and Retrieval
Symptom 3 of Cognitive Science:
Emphasis
• Cognitive scientists deliberately de-emphasis
certain factors which may be important for
cognitive functioning but whose inclusion
would unnecessarily complicate the cognitivescientific enterprise. These de-emphasized
factors include emotional affect, historical,
cultural, and other types of context (e.g.,
issues of embodiment and the senses).
(adapted from Gardner, 1986)
30 Oct 2001
IS202: Information Organization and Retrieval
Symptom 4 of Cognitive Science:
Epistemology
• Cognitive science is concerned with an
area that has historically been a part of
philosophy, namely the domain of
epistemology.
(adapted from Gardner, 1986)
30 Oct 2001
IS202: Information Organization and Retrieval
Symptom 5 of Cognitive Science:
Interdisciplinarity
• Cognitive science is an interdisciplinary
enterprise.
(adapted from Gardner, 1986)
30 Oct 2001
IS202: Information Organization and Retrieval
The disciplines of
cognitive science
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Philosophy
Psychology
Artificial Intelligence
Linguistics
Anthropology
Neuroscience
30 Oct 2001
IS202: Information Organization and Retrieval
The birth of Cognitive Science
• Symposium on Information Theory, MIT,
10-12 September 1956
– Allen Newell & Herbert Simon, “Logic
Theory Machine”
– Noam Chomsky, “Three Models of
Language”
– George Miller, “The Magical Number
Seven”
30 Oct 2001
IS202: Information Organization and Retrieval
The birth of AI
• Rockefeller-sponsored Institute at Dartmouth
College, Summer 1956
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John McCarthy, Dartmouth (->MIT->Stanford)
Marvin Minsky, MIT (geometry)
Herbert Simon, CMU (logic)
Allen Newell, CMU (logic)
Arthur Samuel, IBM (checkers)
Alex Bernstein, IBM (chess)
Nathan Rochester, IBM (neural networks)
Etc.
30 Oct 2001
IS202: Information Organization and Retrieval
Definition of AI
“... artificial intelligence [AI] is the science
of making machines do things that
would require intelligence if done by
[humans]” (Minsky, 1963)
30 Oct 2001
IS202: Information Organization and Retrieval
Some areas of AI
• Knowledge Representation
• Programming Languages
• Natural Language Understanding
• Speech Understanding
• Vision
• Robotics
• Planning
• Machine Learning
• Expert Systems
30
2001
IS202:
Information Organization and Retrieval
• Oct
Qualitative
Simulation
Common Sense
(according to AI)
•
The advice taker is a proposed program for solving problems by
manipulating sentences in formal languages. The main advantages we
expect the advice taker to have is that its behavior will be improvable
merely by making statements to it, telling it about its symbolic
environment and what is wanted from it. To make these statements will
require little if any knowledge of the program or the previous knowledge
of the advice taker. One will be able to assume that the advice taker will
have available to it a fairly wide class of immediate logical
consequences of anything it is told and its previous knowledge. This
property is expected to have much in common with what makes us
describe certain humans as having common sense. We shall therefore
say that a program has common sense if it automatically deduces for
itself a sufficiently wide class of immediate consequences of anything it
is told and what it already knows.
John McCarthy, “Programs with Common Sense,” 1959
30 Oct 2001
IS202: Information Organization and Retrieval
Common Sense:
The original motivation
Before describing the advice taker in
any detail, I would like to describe more
fully our motivation for proceeding in
this direction. Our ultimate objective is
to make programs that learn from their
experience as effectively as humans do.
John McCarthy, “Programs with Common Sense,” 1959
30 Oct 2001
IS202: Information Organization and Retrieval
Commonsense as Interface
• To make our computers easier to use, we must make them more
sensitive to our needs. That is, make them understand what we
mean when we try to tell them what we want. … If we want our
computers to understand us, we’ll need to equip them with
adequate knowledge.
Marvin Minsky, “Commonsense-based Interfaces,” 2000
30 Oct 2001
IS202: Information Organization and Retrieval
What is common sense?
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Whenever we speak about "commonsense thought," we're referring to things
that most people can do, often not even knowing they're doing them. Thus,
when you hear a sentence like: "Fred told the waiter he wanted some chips,“
you will infer all sorts of things. Here are just a few of these…
The word "he" means Fred. That is, it's Fred who wants the chips, not the waiter.
This event took place in a restaurant. Fred was a customer dining there at that
time. Fred and the waiter were a few feet apart at the time. The waiter was at
work there, waiting on Fred at that time. Fred wants potato chips, not wood
chips, cow chips, or bone chips. There's no particular set of chips he wants.
Fred wants and expects the waiter to bring him a single portion (1–5 ounces, 5–
25 chips) in the next few minutes. Fred will start eating the chips very shortly
after he gets them.
Fred accomplishes this by speaking words to the waiter. Fred and the waiter
speak the same language. Fred and the waiter are both human beings. Fred is
old enough to talk (2+ years of age). The waiter is old enough to work (4+ years,
probably 15+). This event took place after the date of invention of potato chips
(in 1853).
Fred assumes the waiter also infers all those things.
Marvin Minsky, “Commonsense-based Interfaces,” 2000
30 Oct 2001
IS202: Information Organization and Retrieval
Can common sense
be coded?
• www.openmind.org
• ThoughtTreasure: www.signiform.com
30 Oct 2001
IS202: Information Organization and Retrieval
Attempts to code
large bodies of knowledge:
some previous examples
• 18th C.: The French Encyclopediasts: Denis
Diderot & Jean D’Alembert
size: 20.8 million words, 400,000 unique forms, 18,000
pages of text, 17 volumes of articles, 11 volumes of plate
legends, 140 contributors
• 19th C.: Thesaurus: Peter Mark Roget
size: (third edition) 35,000 synonyms and over 250,000
cross-references
• 20th C.: Paul Otlet: Répertoire Bibliographique
Universel (RBU)
size: (1930) 16 millions entries (authors and subjects)
30 Oct 2001
IS202: Information Organization and Retrieval
Knowledge Representation
In AI, a representation of knowledge is a
combination of
• data structures and
• interpretative procedures
that, if used in the right way in a program,
will lead to “knowledgeable” behavior.
(Barr and Feigenbaum, 1981, p. 143)
30 Oct 2001
IS202: Information Organization and Retrieval
“Interpretative Procedures” aka Inference
• Deduction
– Universal instantiation: If something is true of
everything, then it is true for any particular thing.
– Modus ponens:
• Known: (1) the rule if P then Q; and, (2) the fact, P is true;
• Infer: Q is true
• Abduction
– Known: (1) the rule if P then Q; and, (2) the fact, Q
is true;
– Infer: P is true
• Induction: Machine Learning
– Known: P(a) is true; P(b) is true; …
– Infer: Forall X, P(X) is true
30 Oct 2001
IS202: Information Organization and Retrieval
Knowledge Representation
and
Programming Paradigms
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Applicative
Functional
Logical
Rule-based
Constraint-based
Object-oriented
Frame-based
30 Oct 2001
IS202: Information Organization and Retrieval
Applicative
define author-of (title)
if (title == “Modern Information Retrieval”)
then author  [“Baeza-Yates”, “Ribeiro”]
30 Oct 2001
IS202: Information Organization and Retrieval
Functional
define author-of (title)
if (title == “Modern Information Retrieval”)
then return([“Baeza-Yates”, “Ribeiro”])
else return([])
30 Oct 2001
IS202: Information Organization and Retrieval
Logical/Declarative
define author-of (“Modern Information Retrieval”,
“Baeza-Yates”).
define author-of (“Modern Information Retrieval”,
“Ribeiro”).
define author-of(“The Organization of Information”,
“Taylor”).
/* backward chaining */
define publication(Author,Title) :- author-of(Title,Author).
30 Oct 2001
IS202: Information Organization and Retrieval
Rule-Based
assert author-of (“Modern Information Retrieval”,
“Baeza-Yates”).
assert author-of (“Modern Information Retrieval”,
“Ribeiro”).
assert author-of(“The Organization of Information”,
“Taylor”).
/* forward chaining */
author-of(Title,Author)  assert publication(Author,Title).
30 Oct 2001
IS202: Information Organization and Retrieval
Object-oriented
define author (Name, Publications)
Name isa String
Publications isa List
define get-publications
return Publications
/* and/or the other way around */
define publication (Title, Authors)
Title isa String
Authors isa List
define get-author
return Authors
courseText = new publication(“Modern Information Retrieval”,
[“Baeza-Yates”, “Ribeiro”]);
30 Oct 2001
IS202: Information Organization and Retrieval
Frame-based
has-prototype(publications, list)
has-prototype(authors,list)
has-prototype(inverse,singleton)
inverse(inverse,inverse)
inverse(authors,publications)
has-prototype(Modern-Information-Retrieval,
singleton)
has-prototype(Baeza-Yates,singleton)
has-prototype(Ribeiro,singleton)
authors(Modern-Information-Retrieval,Baeza-Yates)
authors(Modern-Information-Retrieval,Ribeiro)
? get(ribeiro,publications)
30 Oct 2001
IS202: Information Organization and Retrieval
Cyc’s top-level Ontology
• http://www.cyc.com/cyc-2-1/toc.html
30 Oct 2001
IS202: Information Organization and Retrieval
Common Sense Knowledge
Representation: Examples
• Example 1: Story Understanding: SAM,
Cullingford et al., 1979
www.sims.berkeley.edu/~sack/Code/Lisp/micro-sam.lisp
• Example 2: Story Generation: Talespin,
1976
www.sims.berkeley.edu/~sack/Code/Lisp/micro-talespin.lisp
30 Oct 2001
IS202: Information Organization and Retrieval
Examples of Talespin’s
missing common sense
(Meehan, 1976)
• Answers to questions can take more than one
form.
• Don’t always take answers literally.
• You can notice things without being told about
them.
• Gravity is not a living creature.
• Stories aren’t really stories if they don’t have
a central problem.
• Sometimes enough is enough.
•30 Oct
Schizophrenia
can disfunctional.
2001
IS202: Information Organization and Retrieval
Next Time
• Cognitive Science continued: WordNet
30 Oct 2001
IS202: Information Organization and Retrieval