Knowledge Representation
The Edwin Smith papyrus
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Title:
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Symptoms:
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If you examine a man with a fracture of the cheekbone, you will
find a salient and red fluxion, bordering the wound.
Diagnosis and prognosis:
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Instructions for treating a fracture of the cheekbone.
Then you will tell your patient: "A fracture of the cheekbone. It is
an injury that I will cure."
Treatment:
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You shall tend him with fresh meat the first day. The treatment
shall last until the fluxion resorbs.
Next you shall treat him with raspberry, honey, and bandages to
be renewed each day, until he is cured.
Searle’s Chinese Room
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http://www.mind.ilstu.edu/curriculum/searle_chinese_room/searle_chinese_room.p
hp
Monolingual English speaker locked in a room,
Given
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Those giving you the symbols call
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a large batch of Chinese writing
a second batch of Chinese script
a set of rules in English for correlating the second batch with the first batch.
A third batch of Chinese symbols and more instructions in English enable you "to
correlate elements of this third batch with elements of the first two batches" and instruct
you, thereby, "to give back certain sorts of Chinese symbols with certain sorts of shapes in
response."
the first batch 'a script' [a data structure with natural language processing applications],
the second batch 'a story',
the third batch 'questions';
the symbols you give back the ‘answers to the questions’
the set of rules in English ‘the program‘
Can you be considered to understand Chinese ?
Approaches to Artificial Intelligence
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Cognitive Scientists
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Engineers
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Want to build machines with human-like intelligence
Weak AI
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want computers to do very smart things, quite independently of how
humans work
Strong AI
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think AI is the only serious way of finding out how humans work
Want to build machines that exhibit intelligent like behaviour but
believe machines will always be intellectually inferior to humans
Computer as a metaphor for the mind has been the dominant
approach for the last 60 years
Weak Vs. Strong AI - Philosopher John
Searle
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WEAK AI
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like Cognitive Science above (I.e. about people)
uses machine representations and hypotheses to mimic human
mental function on a computer , but never ascribes those
mental properties to the machine.
STRONG AI
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claim that machines programmed with the appropriate
behaviour, are having the same mental states as people would
who have the same behaviour
i.e. that machines can have MENTAL STATES.
What is Artificial Intelligence ?
Make machines behave as they do in the movies!
 About the emulation of human behaviour
 Make machines do things that would require intelligence if
done by humans
Boden, M.A. (1977). Artificial Intelligence and Natural Man. Basic
Books, New York.
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Concerned with programming computers to perform tasks
presently done better by humans because they involve higher
mental processes such as perceptual learning, memory
organisation and judgemental reasoning
Minsky, M.L. and Papert, S.A. (1969). Perceptrons. MIT Press,
Cambridge, MA.
What is Artificial Intelligence ?
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Agreement that it is concerned with two things
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Studying human thought processes
Representing these processes via machines
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Computers
Robots
Artificial Intelligence is behaviour by a machine which if
performed by a human would be considered intelligent
“Artificial Intelligence is the study of how to make
computers do things at which, at the moment, people are
better”
Elaine Rich, Artificial Intelligence, McGraw-Hill, 1983, p. 1
What are humans better at ?
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Playing Games
Solving Puzzles
Common Sense Reasoning
Expert Reasoning
Understanding Language
Learning
AI
Psychology
Philosophy
AI
Anthropology
Linguistics
Computer Science
Neuro-Science
Intelligence
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How does a human mind work ?
Can non-humans have minds ?
In terms of computing philosophy
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Accept the idea that machines can do anything
Oppose this – machines incapable of sophisticated behaviour e.g.
love, creativity
OK in philosophy
How about in engineering/science terms ?
Intelligence
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What does intelligence mean ?
Dictionary definition
1.
2.
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Someone’s ability to understand and learn things
Intelligence is the ability to think and understand instead of
doing things by instinct or automatically
(Collins English Dictionary)
1st => possessed by humans Someone’s
2nd => some flexibility, does not specify someone
Intelligence – what is thinking ?
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Thinking
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=> have to have a brain
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Activity of using your brain to consider a problem or create an
idea (Collins)
Organ that allows learning and understanding
Is it possible for machines to achieve this ?
Can machines think ?
Why build intelligent machines?
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Cheaper to build and maintain
Offer new possibilities
Better solutions to problems
Software relatively cheap to develop
Software can be changed easily
Why is AI relevant to us ?
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Ai is concerned with how
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knowledge is acquired and used,
information is communicated,
collaboration is achieved,
how problems are solved,
languages are developed, etc.
History of AI (Classical Period or Dark Ages)
mid 1940’s – mid 1950’s
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Game Playing & Theorem Proving
State Space Searching
Alan Turing
McCulloch & Pitts
Von Neumann
Turing
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Proposed the concept of a universal machine
Mathematical Tool equivalent to Digital Computer
Takes input and computes output via a Finite State
Machine
Must construct a different machine for each computation
Turing
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Enigma Machine
Wrote the first program capable of playing a complete
chess game;
Reflections on intelligence:
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Is there thought without experience?
Is there mind without communication?
Is there language without living?
Is there intelligence without life?
i.e. can machines think?
Turing
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Invented a game ‘Turning Imitation Game’
Can machines pass a behaviour test for intelligence
Defined the intelligent behaviour of a computer as the
ability to achieve the human-level performance in
cognitive tasks
Predicted that by 2000 a computer could be programmed
to have a conversation with a human interrogator for five
minutes and would have a 30 per cent chance of
deceiving the interrogator that it was a human
The Turing Test
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Computer passes the test if interrogators cannot distinguish the machine from a
human on the basis of the answers to their questions.
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Original Game:
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First phase
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Interrogator, a man and a woman are each placed in separate rooms and can communicate only via a
neutral medium such as a remote terminal.
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Interrogator’s objective is to work out who is the man and who is the woman by questioning them.
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Man should attempt to deceive the interrogator that he is the woman, while the woman has to
convince the interrogator that she is the woman.
Second phase
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Man is replaced by a computer programmed to deceive the interrogator as the man did.
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Programmed to make mistakes and provide fuzzy answers in the way a human would.
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If the computer can fool the interrogator as often as the man did, we may say this computer has
passed the intelligent behaviour test.
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Interrogator does not see, touch or hear the computer and is therefore not
influenced by its appearance or voice
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Annually The Lobner Prize - http://www.loebner.net/Prizef/loebner-prize.html
McCulloch & Pitts
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Proposed model of artificial neural networks in which
each neuron was postulated as being in binary state, that
is, in either on or off condition
Demonstrated that their neural network model was, in
fact, equivalent to the Turing machine, and proved that any
computable function could be computed by some
network of connected neurons
McCulloch & Pitts
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Stimulated both theoretical and experimental work to
model the brain in the laboratory.
Experiments clearly demonstrated that the binary model
of neurons was not correct
Von Neumann
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Part of the Manhattan Project
Adviser for the Electronic Numerical Integrator and
Calculator (ENIAC) project at the University of Pennsylvania
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First general purpose computer
Helped to design the Electronic Discrete Variable Automatic
Computer (EDVAC), a stored program machine.
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Binary rather than decimal
History of AI (Great Expectations)
(mid 50’s – late 60’s)
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John McCarthy
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Marvin Minsky
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Continuing work on neural networks
Learning methods improved
Newell & Simon
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Focus on formal logic
Developed anti-logic outlook on knowledge representation and reasoning
Frames
McCulloch & Pitts
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Inventor of LISP
AdviceTaker – first complete knowledge-based system
General Problem Solver(GPS) – simulate human problem solving
Based on technique of means-end analysis
Choose and apply operators to achieve goal state
Focus on general problem solving, weak AI
History of AI (Great Expectations)
(mid 50’s – late 60’s)
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Newell & Simon
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Attempts to separate problem solving from data
Proposed that a problem to be solved could be defined in
terms of states.
Means-ends analysis was used to determine a difference
between the current state and the desirable state or the goal
state of the problem, and to choose and apply operators to
reach the goal state.
If the goal state could not be immediately reached from the
current state, a new state closer to the goal would be
established and the procedure repeated until the goal state was
reached.
The set of operators determined the solution plan.
History of AI (Great Expectations)
(mid 50’s – late 60’s)
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Newell & Simon
 GPS failed to solve complicated problems.
 Program was based on formal logic and therefore could
generate an infinite number of possible operators, which
is inherently inefficient.
 The amount of computer time and memory that GPS
required to solve real-world problems led to the project
being abandoned.
History of AI (Reality Strikes)
(late 60’s – early 70’s)
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AI researchers were developing general methods for
broad classes of problems
Programs contained little or no knowledge about
problem domain
Applied a search strategy by trying different combinations
of steps until right one found
Problems chosen too broad and too difficult
History of AI (Expert Systems)
(early 70’s – mid 80’s)
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Realisation that problem domain must be restricted
Feigenbaum & Buchanan
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DENDRAL program developed at Stanford to analyse chemicals
Incorporated knowledge of expert into program to perform at human
expert level
Shift from weak methods
Difficult – knowledge acquisition
Shortliffe
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MYCIN – rule-based expert system for the diagnosis of infectious
diseases
Rules reflected uncertainty
History of AI (Making Machines Learn)
(mid 80’s - )
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Expert Systems require more than rules
Rebirth of neural networks
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Technology assisted
Evolutionary computing
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Learning by doing
Ongoing since 70s
Natural intelligence is product of evolution
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Based on computational models of natural selection and genetics
Simulate populate, evaluate performance, generate new population
Concept introduced by John Holland in 1975
History of AI (Making Machines Learn)
(1980’s onwards)
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Knowledge Engineering
Computing with Words
Handling Uncertainty
Improved computational power
Improved cognitive modelling
The ability to represent multiple experts
Today
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Topics in AI are much the same
Language now not so near the centre but it was at the
centre in the 70s
Roots now much further from logic and theorem proving
Neural nets and machine learning now more central
AI Approaches transitioned to main stream
What has AI achieved in real world ?
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Robots in manufacturing
Diagnosis of illness: screen lab tests, diagnose blood
infections, identify tumors
Run airports: e.g. assign baggage gates, direct re-fuelling
Reasonable machine translation
Search systems like Google – efficient information
retrieval
Computer games
Deep Blue beat Kasparov in 1997
Key Lessons
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Intelligence = ability to learn and understand, to solve
problems and to make decisions.
Goal of AI = making machines do things that would
require intelligence if done by humans.
A machine is thought intelligent if it can achieve humanlevel performance in some cognitive task.
To build an intelligent machine, we have to capture,
organise and use human expert knowledge in some
problem area.
Negnevitsky M 2005, Artificial Intelligence, A guide to
intelligent systems design, 2nd Edition, Addison Welsey
Why Representation?
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Humans need words (or symbols) to communicate
efficiently
Mapping of words to things is only possible indirectly
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Create concepts that refer to things
What is knowledge representation?
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What is representation?
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When do we need to represent?
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Representation refers to a symbol or thing which represents
(’refers to’, ’stands for’) something else.
We need to represent a thing in the natural world when we
don’t have, for some reason, the possibility to use the original
’thing’.
Example: Planning ahead – how will our actions affect the
world, and how will we reach our goals?
The object of knowledge representation is to express the
problem in computer-understandable form
Aspects of KR
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Syntactic
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Semantic
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Possible (allowed) constructions
Each individual representation is often called a sentence.
For example: color(my_car, red), my_car(red), red(my_car), etc.
What does the representation mean (maps the sentences to the
world)
For example:
color(my_car, red) → ??
‘my car is red’, ‘paint my car red’, etc.
Inferential
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The interpreter
Decides what kind of conclusions can be drawn
For example: Modus ponens (P, P→Q, therefore Q)
Well-defined syntax/semantics
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Knowledge representation languages should have precise
syntax and semantics.
You must know exactly what an expression means in
terms of objects in the real world.
Real World
Real World
Map to
KR language
Representation
of facts in the world
Map back to
real world
Inference
New
conclusions
Declarative vs. Procedural
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Declarative knowledge (facts about the world)
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A set of declarations or statements.
All facts stated in a knowledge base fall into this category of
knowledge.
In a sense, declarative knowledge tells us what a problem (or
problem domain) is all about
Procedural knowledge (how something is done)
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Something that is not stated but which provides a mean of
dynamically (usually at run-time) arriving at new facts.
Declarative example
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Information about items in a store
cheaper(coca_cola, pepsi)
tastier(coca_cola, pepsi)
if (cheaper(x,y) && (tastier(x,y) ) →
buy(x)
Procedural example
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Shopping script:
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Make a list of all items to buy
Walk to the shop
For each item on the list, get the item and add it to the
shopping basket
Walk to the checkout counter
Pack the items
Pay
Walk home
Types of knowledge
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Domain knowledge:
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What we reason about
Structural knowledge
 Organization of concepts
Relational knowledge
 How concepts relate
Strategic knowledge:
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How we reason
At representation level, rather than at implementation level
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conflicting situations)
What is a Knowledge Representation?
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“What is a Knowledge Representation?”
(Davis, Shrobe &Szolovits) AI Magazine, 14(1):17-33, 1993
http://groups.csail.mit.edu/medg/ftp/psz/k-rep.html
Defines the five roles the knowledge representation plays
Each role defines characteristics a KR should have
These roles provide a framework for comparison and
evaluating KRs
Role I: A KR is a Surrogate
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A KR is used to model objects in the world.
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Substitute for direct interaction with the world.
Cannot possibly represent everything in the world, a KR must
necessarily focus on certain objects and properties while
ignoring others.
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As a result only objects and properties that are relevant to reasoning
are modeled.
Consequences:
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Representation is not perfect
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will have errors (at least by omission) and we may even introduce
new artifacts which not present
At least some unsound reasoning will occur
Role I: A KR is a Surrogate
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The only complete accurate representation of an object is
the object itself.
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All other representations are inaccurate.
Role II: A KR is a set of
Ontological Commitments
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All representations are approximations to reality and they
are invariably imperfect.
Therefore we need to focus on only some parts of the
world, and ignore the others.
Ontological commitments determine what part of
the world we need to look at, and how to view it.
Role II: A KR is a set of
Ontological Commitments
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The ontological commitments are accumulated in layers:
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First layer – representation technologies.
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For example, logic or semantic networks (entities and relations) vs.
frames (prototypes)
Second layer – how will we model the world.
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Example from a frame-based system:
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“The KB underlying INTERNIST system is composed of two basic types of
elements: disease entities and manifestations […] It also contains a hierarchy of
disease categories organised primarily around the concept of organ systems
having at the top level such categories as ’liver disease’, ’kidney disease’, etc”
Commits to model prototypical diseases which will be organised in a taxonomy
by organ failure
Third layer (conceptual) – which objects will be modelled.
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What is considered a disease (abnormal state requiring cure), e.g.
alcoholism, chronic fatigue syndrome?
Role III : A KR is a Fragmentary
Theory of Intelligent Reasoning
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“What is intelligent reasoning?”
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The views of intelligence normally come from fields outside of
AI: mathematics, psychology, biology, statistics and economics.
Fragmentary
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the representation typically incorporates only part of the
insight or belief that motivated it
that insight or belief is in turn only a part of the complex and
multi-faceted phenomenon of intelligent reasoning.
Role III : A KR is a Fragmentary
Theory of Intelligent Reasoning
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There are three components:
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the representation's fundamental conception of intelligent
inference
(What does it mean to reason intelligently?)
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the set of inferences the representation sanctions
(What can we infer from what we know?)
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the set of inferences it recommends
(What ought we to infer from what we know?)
Role IV: A KR is a medium for
efficient computation
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The knowledge representation should make
recommended inferences efficient.
The information should be organized in such a way to
facilitate making those inferences.
There is usually a tradeoff between
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the power of expression (how much can be expressed and
reasoned about in a language) and
how computationally efficient the language is.
Role V: A KR is a medium of
human expression
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A representation is a language in which we communicate.
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How well does the representation function as a medium of
expression?
How general is it?
How precise?
Does it provide expressive adequacy?
How well does it function as a medium of
communication?
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How easy is it for us to ‘talk’ or think in that language?
Consequences of this KR
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The spirit should be indulged, not overcome –
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Representation and reasoning are intertwined
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a recommended method of inference is needed to make sense
of a set of facts.
Some researchers claim equivalence between KRs, i.e.
“frames are just a new syntax for first-order logic”.
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KRs should be used only in ways that they are intended to be
used, that is the source of their power.
However, such claims ignore the important ontological
commitments and computational properties of a
representation.
All five roles of a KR matter
Randall Davis, Howard Shrobe, Peter Szolovits MIT Lab
Requirements for KR languages
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Representation adequacy
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Inferential adequacy
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inferences should be efficient
Clear syntax and semantics
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should allow inferring new knowledge
Inferential efficiency
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should to allow for representing all the required knowledge
unambiguous and well-defined syntax and semantics
Naturalness
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easy to read and use
Some Knowledge Representation
Formalisms
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Production systems, expert systems
Semantic networks
Frames
Case-based reasoning
Biologically inspired approaches
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neural networks
genetic algorithms