Transcript Artificial Intelligence, Ontologies, and Common Sense

Lecture 04: Knowledge Representation
SIMS 202:
Information Organization
and Retrieval
Prof. Ray Larson & Prof. Marc Davis
UC Berkeley SIMS
Tuesday and Thursday 10:30 am - 12:00 am
Fall 2002
Credits to Warren Sack for some of the slides in this lecture
IS 202 - Fall 2002
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Today
• Review of Categorization
• From Cognitive Science to AI
• The Vocabulary Problem
• Artificial Intelligence, Knowledge
Representation,and Commonsense
• Photo Project Assignment 2 Check-In
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Categorization
• Processes of categorization are fundamental to
human cognition
• Categorization is messier than our computer
systems would like
• Human categorization is characterized by
– Family resemblances
– Prototypes
– Basic-level categories
• Considering how human categorization functions
is important in the design of information
organization and retrieval systems
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Categorization
• Classical categorization
– Necessary and sufficient conditions for
membership
– Generic-to-specific monohierarchical structure
• Modern categorization
– Characteristic features (family resemblances)
– Centrality/typicality (prototypes)
– Basic-level categories
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Properties of Categorization
• Family Resemblance
– Members of a category may be related to one
another without all members having any
property in common
• Prototypes
– Some members of a category may be “better
examples” than others, i.e., “prototypical”
members
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Basic-Level Categorization
• Perception
– Overall perceived shape
– Single mental image
– Fast identification
• Function
– General motor program
• Communication
– Shortest, most commonly used and contextually neutral words
– First learned by children
• Knowledge Organization
– Most attributes of category members stored at this level
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Information Hierarchy
Wisdom
Knowledge
Information
Data
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Information Hierarchy
Wisdom
Knowledge
Information
Data
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Today’s Thinkers/Tinkerers
George Furnas
http://www.si.umich.e
du/~furnas/
Marvin Minsky
http://web.media.mit.
edu/~minsky/
Doug Lenat
http://www.cyc.com/st
aff.html
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Psychology Methodology
Theorizing
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Experimenting
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Computer Science Methodology
Theorizing
System Building
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Cognitive Science Methodology
Theorizing
Experimenting
System Building
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What is Cognitive Science?
• 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
– (1) Mental representations
– (2) Computers
– (3) Emphasis
– (4) Epistemology
– (5) Interdisciplinarity
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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)
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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)
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Symptom 3 of Cognitive Science:
Emphasis
• Cognitive scientists deliberately deemphasize certain factors which may be
important for cognitive functioning but
whose inclusion would unnecessarily
complicate the cognitive-scientific
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)
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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)
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Symptom 5 of Cognitive Science:
Interdisciplinarity
• Cognitive science is an interdisciplinary
enterprise.
(adapted from Gardner, 1986)
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Disciplines of Cognitive Science
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Philosophy
Psychology
Artificial Intelligence
Linguistics
Anthropology
Neuroscience
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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”
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The Birth of AI
• Rockefeller-sponsored Institute at
Dartmouth College, Summer 1956
– 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.
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Definition of AI
“... artificial intelligence [AI] is the science of
making machines do things that would
require intelligence if done by [humans]”
(Minsky, 1963)
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The Goals of AI Are Not New
• Ancient Greece
– Daedalus’ automata
• Judaism’s myth of the Golem
• 18th century automata
– Singing, dancing, playing chess?
• Mechanical metaphors for mind
– Clock
– Telegraph/telephone network
– Computer
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Some Areas of AI
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Knowledge Representation
Programming Languages
Natural Language Understanding
Speech Understanding
Vision
Robotics
Planning
Machine Learning
Expert Systems
Qualitative Simulation
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Furnas: The Vocabulary Problem
• People use different words to describe the
same things
– “If one person assigns the name of an item,
other untutored people will fail to access it on
80 to 90 percent of their attempts.”
– “Simply stated, the data tell us there is no one
good access term for most objects.”
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The Vocabulary Problem
• How is it that we come to understand each
other?
– Shared context
– Dialogue
• How can machines come to understand
what we say?
– Shared context?
– Dialogue?
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Vocabulary Problem Solutions?
• Furnas et al.
– Make the user memorize precise system
meanings
– Have the user and system interact to identify
the precise referent
• Minsky and Lenat
– Give the system “commonsense” so it can
understand what the user’s words can mean
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Lenat on the Vocabulary Problem
• “The important point is that users will be
able to find information without having to
be familiar with the precise way the
information is stored, either through field
names or by knowing which databases
exist, and can be tapped.”
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Minsky on the Vocabulary Problem
• “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.”
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Commonsense
• Commonsense is background knowledge
that enables us to understand, act, and
communicate
• Things that most children know
• Minsky on commonsense:
– “Much of our commonsense knowledge
information has never been recorded at all
because it has always seemed so obvious we
never thought of describing it.”
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Commonsense Example
• “I want to get inexpensive dog food.”
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The food is not made out of dogs.
The food is not for me to eat.
Dogs cannot buy their own food.
I am not asking to be given dog food.
I am not saying that I want to understand
why some dog food is inexpensive.
• The dog food is not more than $5 per can.
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Engineering Commonsense
• Use multiple ways to represent knowledge
• Acquire huge amounts of that knowledge
• Find commonsense ways to reason with it
(“knowledge about how to think”)
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CYC
• Decades long effort to build commonsense
knowledge-base
• Storied past
• 100,000 basic concepts
• 1,000,000 assertions about the world
• The validity of Cyc’s assertions are
context-dependent (default reasoning)
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Cyc’s Top-Level Ontology
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Fundamentals
Top Level
Time and Dates
Types of Predicates
Spatial Relations
Quantities
Mathematics
Contexts
Groups
"Doing"
Transformations
Changes Of State
Transfer Of
Possession
Movement
Parts of Objects
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Professions
Composition of
Substances
Agents
Organizations
Actors
Roles
Emotion
Propositional
Attitudes
Social
Biology
Chemistry
Physiology
General
Medicine
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Materials
Waves
Devices
Construction
Financial
Food
Clothing
Weather
Geography
Transportation
Information
Perception
Agreements
Linguistic Terms
Documentation
http://www.cyc.com/cyc-2-1/toc.html
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OpenCYC
• Cyc’s knowledge-base is now coming
online
– http://www.opencyc.org/
• How could Cyc’s knowledge-base affect
the design of information organization and
retrieval systems?
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Multiple Representations
• Minksy
– “I think this is what brains do instead: Find several
ways to represent each problem and to represent the
required knowledge. Then when one method fails to
solve a problem, you can quickly switch to another
description.”
• Furnas
– “But regardless of the number of commands or
objects in a system and whatever the choice of their
‘official’ names, the designer must make many, many
alternative verbal access routes to each.”
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AI or IA?
• Artificial Intelligence (AI)
– Make machines as smart as (or smarter than)
people
• Intelligence Amplification (IA)
– Use machines to make people smarter
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Assignment 0 Check-In
• Deliverables
– Personal web page
– Assignments page
– Email address
– Focus statement
– Online Questionnaire
• Feedback
– Spell-check and grammar-check
– Simple vs. skeletal
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Assignment 2 Check-In
• Deliverables
– Persona description (brief)
– Scenario description (brief)
– Annotated user experience storyboard
– Group web site
– Work distribution table on your group web site
– Photos for your application idea
• Feedback
– Questions, comments, problems?
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Homework (!)
• Read
– Chapters 3 and 5 in The Organization of
Information (OI)
• Assignment 2: Photo Use Scenario
– Due by Thursday, September 12
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Next Time
• Metadata Introduction (RRL)
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