Transcript Inteligencia Artificial

Artificial Intelligence
Knowledge representation
Fall 2008
professor: Luigi Ceccaroni
Introduction
• Knowledge engineers and system
analysts need to bring knowledge forth
and make it explicit. (Why?)
• They display the implicit knowledge about
a subject in a form that programmers can
encode in algorithms and data structures.
• To make the hidden knowledge accessible
to computers, knowledge-based
systems and object-oriented systems
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are needed.
Introduction
• Knowledge-based and object-oriented systems
are built around declarative languages:
– Forms of expression closer to human languages
• Such systems help to express the knowledge in
a form that both humans and computers can
understand.
• This part of the course is about knowledge-base
analysis and design:
– To analyze knowledge about the real world and map
it to a computable form
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Logic, ontology and
computation
• Knowledge representation (KR) is an
interdisciplinary subject that applies
theories and techniques from three fields:
– Logic provides the formal structure and
rules of inference.
– Ontology defines the kinds of things that
exist in the application domain.
– Computation supports the applications that
distinguish KR from pure philosophy.
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Principles of knowledge
representation
• Knowledge engineering is the application
of logic and ontology to the task of
building computable models of some
domain for some purpose.
• In 1993, three experts in KR, Davis,
Schrobe and Szolovits, wrote a critical
review and analysis of the state of the art:
– Five basic principles about knowledge
representations (KRs) and their role in
artificial intelligence
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What is a
knowledge representation?
1. A surrogate
• Imperfect surrogates mean incorrect inferences are
inevitable
2. A set of ontological commitments
• Commitment begins with the earliest choices
• The commitments accumulate in layers
• Reminder: a KR is not a data structure
3. A fragmentary theory of intelligent reasoning
• What is intelligent reasoning?
• Which inferences are sanctioned?
• Which inferences are recommended?
4. A medium for efficient computation
5. A medium of human expression
A KR is a surrogate
• Description of something else
• Abstract, simplified view of a domain
• Symbolic structure with formal symbol-manipulating
rules
– Rules are based only on the syntactic form of the
representation
• Requires specification of mapping to intended
referent:
– an interpretation
• Contains simplifying assumptions and inaccuracies
• Susceptible to supporting incorrect reasoning
results
A KR is a set of ontological
commitments
• What to consider in thinking about a world:
concepts, relations, objects
– Example: representing an electric circuit
• “Lumped element model”
– Components with connections between them
– Signals flowing instantaneously along the connections
• “Electrodynamics model”
– Signals propagating at finite speeds
– Locations of and distances between components
– Components through which electromagnetic waves flow
• KR is not about data structures
A KR is a set of ontological
commitments
An ontological commitment is an agreement
to use a vocabulary (i.e., ask queries and
make assertions) in a way that is consistent
(but not complete) with respect to the
theory specified by an ontology. We build
agents that commit to ontologies. We
design ontologies so we can share
knowledge with and among these agents.
Tom Gruber (KSL, Stanford)
A KR is a fragmentary theory of
intelligent reasoning
• It provides different strategies for
reasoning.
– These strategies can be used by humans and
computers.
• It sanctions a set of inferences.
– “What can we infer from what we know?”
• It recommends a set of inferences.
– “What ought we to infer from what we know?”
A KR is a medium for efficient
computation
• Reasoning in machines is a computational
process:
– Both the procedural and the declarative
approaches can be transformed to a
computable form.
• Computational efficiency is a central
design goal.
• Expressivity and tractability of reasoning
are traded off.
A KR is a medium of human
expression
• How useful is it as a medium of expression?
– How general is it?
– How precise is it?
– For what tasks does it provide expressive adequacy?
• How useful is it as a medium of communication?
– Can we easily “talk” or think in the representation
language?
– What kinds of things are easily said in the language?
– What kinds of things are so difficult to say in the
language as to be pragmatically impossible?
KRs vs. data bases
• Both “represent” knowledge.
• Standard data bases do not contain:
– disjunctions (e.g., “The ball is either red or blue.”)
– quantifiers (e.g., “Every person has two parents.”)
• Data base schema provide some quantified information
• Deductive data bases include implications
• Data base research concerns:
– Efficient access and management of large distributed data
bases
– Concurrent updating
• KR research concerns:
– Expressivity
– Effective reasoning
What is a knowledge base
(KB)?
• An informal term for a collection of
information that includes an ontology as
one component.
• Besides an ontology, a KB may contain
information specified in a declarative
language such as logic or expert-system
rules.
• It may also include unstructured or
unformalized information expressed in
natural language or procedural code. 14
Issues in KR research
• What knowledge needs to be represented
to answer given questions?
• How is incomplete or noisy information
represented?
• How is qualitative or abstracted knowledge
represented?
• How can knowledge be encoded so that it
is reusable?
• How are assumptions represented and
reasoned with?
Issues in KR research
• How can knowledge be reformulated for a
given purpose?
• How can effective automatic reasoning be
done with large-scale knowledge bases?
• How can computer-interpretable
knowledge be extracted from documents?
• How can knowledge from multiple sources
be combined and used?
Issues in KR research
• This is a world where massive amounts of
data and applied mathematics replace every
other tool:
– Out with every theory of human behavior, from
linguistics to sociology.
– Forget taxonomy, ontology, and psychology.
• Who knows why people do what they do?
• The point is they do it, and we can track and
measure it with unprecedented fidelity.
• With enough data, the numbers speak for
themselves.
Chris Anderson
Historical background
• The words knowledge and representation have
provoked philosophical controversies for over
2500 years.
• 500 B.C.: Socrates claims to know very little, if
anything.
• He destroyed the self-satisfaction of people who
claimed to have knowledge of fundamental
subjects like:
–
–
–
–
Truth
Beauty
Virtue
Justice
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Historical background
• For his impiety in questioning cherished
beliefs, Socrates was condemned to death as
a corrupter of the morals Athenian youth.
• Socrates’ student Plato established the subject
of epistemology:
– the study of the nature of knowledge and its
justification
• Plato’s student Aristotle shifted the emphasis
of philosophy from the nature of knowledge to
the less controversial but more practical
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problem of representing knowledge.
Historical background
• Aristotle’s work resulted in an encyclopedic
compilation of the knowledge of his day.
• But before he could compile that
knowledge, he had to invent the words for
representing it.
• He established the initial terminology and
defined the scope of logic, physics,
metaphysics, biology, psychology,
linguistics, politics, ethics, rhetoric and
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economics.
Historical background
• Terms that Aristotle coined or adopted have
become the core of today’s international
technical vocabulary:
– category
– metaphor
– hypothesis
– quantity
– quality
– species
– noun
... and then artificial intelligence arrived.
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Early history of KR (60’s - 70’s)
• Origins
– Problem solving work primarily at CMU and MIT
– Natural language understanding
• Many ad hoc formalisms
• “Procedural” vs. “declarative” knowledge
– Procedural: functions, rules, conventional
programming languages
– Declarative: logic, Prolog
• No formal semantics
Emerging paradigms
(70’s - 80’s)
• Semantic nets
– Unstructured node-link graphs
– No semantics to support interpretation
– No axioms to support reasoning
– Reference:
“What’s in a Link: Foundations for Semantic Nets”; Woods, W. A.
In Representation and Understanding: Studies in Cognitive Science;
edited by D. Bobrow and A. Collins; Academic Press; 1975.
Emerging paradigms
(70’s - 80’s)
• Frames
– Structured semantic nets
– Object-oriented descriptions
– Prototypes
– Class-subclass taxonomies
– Reference:
“A Framework for Representing Knowledge” M.
Minsky
Mind Design; J. Haugeland, editor; MIT Press; 1981.
Example: Frames: classsubclass taxonomy
Example: Frames: Class frame
Example: Frames: Instance
frame
Emerging paradigms
(70’s - 80’s)
• Production rule systems
– If-then inference rules
• If (warning-light on) then (engine overheating)
• If (warning-light on) then ((engine overheating)
0.95)
– Situation-action rules
• If (warning-light on) then (turn-off engine)
– Hybrid procedural-declarative representation
– Basis for expert systems
Emerging paradigms
(70’s - 80’s)
• Qualitative physics
– Representing and reasoning:
• With incomplete knowledge
• About physical mechanisms
– Qualitative descriptions
• Capture distinctions that make an important
qualitative difference and ignores others
• Aggregate values that have no qualitative
difference
Emerging paradigms
(70’s - 80’s)
• Symbolic Logic
– Primarily first-order logic
“Everybody loves somebody sometime.”
(forall ?p (implies (Person ?p1)
(exists (?p2 ?t) (and
(Person ?p2)
(Time ?t)
(Loves ?p1 ?p2 ?t)))))
– Resolution theorem proving
KR in the 90’s and 00’s
• Declarative representations
–
–
–
–
Easier to change
Multi-use
Extendable by reasoning
Accessible for introspection
• Formal semantics
– Defines what the representation means
– Specifies correct reasoning
– Allows comparison of representations/algorithms
• KR rooted in the study of logics
– temporal, context, modal, default, nonmonotonic...
• Rigorous theoretical analysis