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Knowledge Representation. The Edwin Smith papyrus. Title: Instructions for treating a fracture of the cheekbone. Symptoms: 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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the edwin smith papyrus
The Edwin Smith papyrus
  • Title:
    • Instructions for treating a fracture of the cheekbone.
  • Symptoms:
    • 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:
    • Then you will tell your patient: "A fracture of the cheekbone. It is an injury that I will cure."
  • Treatment:
    • 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
Searle’s Chinese Room
  • Monolingual English speaker locked in a room,
  • Given
    • 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."
  • Those giving you the symbols call
    • 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
Approaches to Artificial Intelligence
  • Cognitive Scientists
    • think AI is the only serious way of finding outhow humans work
  • Engineers
    • want computers to do very smart things, quite independently of how humans work
  • Strong AI
    • Want to build machines with human-like intelligence
  • Weak AI
    • 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
Weak Vs. Strong AI - Philosopher John Searle
    • 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.
    • 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
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.

  • 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 intelligence1
What is Artificial Intelligence ?
  • Agreement that it is concerned with two things
    • Studying human thought processes
    • Representing these processes via machines
      • 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
What are humans better at ?
  • Playing Games
  • Solving Puzzles
  • Common Sense Reasoning
  • Expert Reasoning
  • Understanding Language
  • Learning







Computer Science

  • How does a human mind work ?
  • Can non-humans have minds ?
  • In terms of computing philosophy
    • 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 ?
  • What does intelligence mean ?
  • Dictionary definition
    • 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

2nd => some flexibility, does not specify someone

intelligence what is thinking
Intelligence – what is thinking ?
  • Thinking
    • Activity of using your brain to consider a problem or create an idea (Collins)
  • => have to have a brain
    • Organ that allows learning and understanding
  • Is it possible for machines to achieve this ?
  • Can machines think ?
why build intelligent machines
Why build intelligent machines?
  • 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
Why is AI relevant to us ?
  • Ai is concerned with how
    • 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
History of AI (Classical Period or Dark Ages) mid 1940’s – mid 1950’s
  • Game Playing & Theorem Proving
  • State Space Searching
  • Alan Turing
  • McCulloch & Pitts
  • Von Neumann
  • 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
  • Enigma Machine
  • Wrote the first program capable of playing a complete chess game;
  • Reflections on intelligence:
    • 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?
  • 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
The Turing Test
  • Computer passes the test if interrogators cannot distinguish the machine from a human on the basis of the answers to their questions.
  • Original Game:
    • First phase
      • 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.
      • Interrogator’s objective is to work out who is the man and who is the woman by questioning them.
      • 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
      • Man is replaced by a computer programmed to deceive the interrogator as the man did.
      • Programmed to make mistakes and provide fuzzy answers in the way a human would.
      • If the computer can fool the interrogator as often as the man did, we may say this computer has passed the intelligent behaviour test.
  • Interrogator does not see, touch or hear the computer and is therefore not influenced by its appearance or voice
  • Annually The Lobner Prize -
mcculloch pitts
McCulloch & Pitts
  • 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 pitts1
McCulloch & Pitts
  • 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
Von Neumann
  • Part of the Manhattan Project
  • Adviser for the Electronic Numerical Integrator and Calculator (ENIAC) project at the University of Pennsylvania
    • First general purpose computer
  • Helped to design the Electronic Discrete Variable Automatic Computer (EDVAC), a stored program machine.
    • Binary rather than decimal
history of ai great expectations mid 50 s late 60 s
History of AI (Great Expectations) (mid 50’s – late 60’s)
  • John McCarthy
      • Inventor of LISP
      • AdviceTaker – first complete knowledge-based system
  • Marvin Minsky
      • Focus on formal logic
      • Developed anti-logic outlook on knowledge representation and reasoning
      • Frames
  • McCulloch & Pitts
      • Continuing work on neural networks
      • Learning methods improved
  • Newell & Simon
      • 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 s1
History of AI (Great Expectations) (mid 50’s – late 60’s)
  • Newell & Simon
    • 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 s2
History of AI (Great Expectations) (mid 50’s – late 60’s)
  • 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
History of AI (Reality Strikes)(late 60’s – early 70’s)
  • 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
History of AI (Expert Systems)(early 70’s – mid 80’s)
  • Realisation that problem domain must be restricted
  • Feigenbaum & Buchanan
      • 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
      • MYCIN – rule-based expert system for the diagnosis of infectious diseases
      • Rules reflected uncertainty
history of ai making machines learn mid 80 s
History of AI (Making Machines Learn)(mid 80’s - )
  • Expert Systems require more than rules
  • Rebirth of neural networks
    • Technology assisted
  • Evolutionary computing
    • Learning by doing
    • Ongoing since 70s
    • Natural intelligence is product of evolution
      • 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
History of AI (Making Machines Learn)(1980’s onwards)
  • Knowledge Engineering
  • Computing with Words
  • Handling Uncertainty
  • Improved computational power
  • Improved cognitive modelling
  • The ability to represent multiple experts
  • 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
What has AI achieved in real world ?
  • 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
Key Lessons
  • 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 human-level 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
Why Representation?
  • Humans need words (or symbols) to communicate efficiently
  • Mapping of words to things is only possible indirectly
    • Create concepts that refer to things
what is knowledge representation
What is knowledge representation?
  • What is representation?
    • Representation refers to a symbol or thing which represents (’refers to’, ’stands for’) something else.
  • When do we need to represent?
    • 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
Aspects of KR
  • Syntactic
    • Possible (allowed) constructions
    • Each individual representation is often called a sentence.
    • For example: color(my_car, red), my_car(red), red(my_car), etc.
  • Semantic
    • 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
    • The interpreter
    • Decides what kind of conclusions can be drawn
    • For example: Modus ponens (P, P→Q, therefore Q)
well defined syntax semantics

Real World

Real World

Map to

KR language

Map back to

real world



of facts in the world



Well-defined syntax/semantics
  • 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.
declarative vs procedural
Declarative vs. Procedural
  • Declarative knowledge (facts about the world)
    • 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)
    • Something that is not stated but which provides a mean of dynamically (usually at run-time) arriving at new facts.
declarative example
Declarative example
  • 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
Procedural example
  • Shopping script:
    • 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
Types of knowledge
  • Domain knowledge:
    • What we reason about
    • Structural knowledge
      • Organization of concepts
    • Relational knowledge
      • How concepts relate
  • Strategic knowledge:
    • How we reason
    • At representation level, rather than at implementation level
      • (e.g. at implementation level – control knowledge, for resolving conflicting situations)
what is a knowledge representation
What is a Knowledge Representation?
  • “What is a Knowledge Representation?”

(Davis, Shrobe &Szolovits) AI Magazine, 14(1):17-33, 1993

  • 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
Role I: A KR is a Surrogate
  • A KR is used to model objects in the world.
    • 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.
      • As a result only objects and properties that are relevant to reasoning are modeled.
  • Consequences:
    • Representation is not perfect
      • 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 surrogate1
Role I: A KR is a Surrogate
  • The only complete accurate representation of an object is the object itself.
  • All other representations are inaccurate.
role ii a kr is a set of ontological commitments
Role II: A KR is a set ofOntological Commitments
  • 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 commitments1
Role II: A KR is a set ofOntological Commitments
  • The ontological commitments are accumulated in layers:
    • First layer – representation technologies.
      • For example, logic or semantic networks (entities and relations) vs. frames (prototypes)
    • Second layer – how will we model the world.
      • Example from a frame-based system:
        • “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.
      • 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
Role III : A KR is a FragmentaryTheory of Intelligent Reasoning
  • “What is intelligent reasoning?”
    • The views of intelligence normally come from fields outside of AI: mathematics, psychology, biology, statistics and economics.
  • Fragmentary
    • 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 reasoning1
Role III : A KR is a FragmentaryTheory of Intelligent Reasoning
  • There are three components:
    • the representation's fundamental conception of intelligent inference

(What does it mean to reason intelligently?)

    • the set of inferences the representation sanctions

(What can we infer from what we know?)

    • 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
Role IV: A KR is a medium forefficient computation
  • 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
    • 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
Role V: A KR is a medium ofhuman expression
  • A representation is a language in which we communicate.
    • 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?
    • How easy is it for us to ‘talk’ or think in that language?
consequences of this kr
Consequences of this KR
  • The spirit should be indulged, not overcome –
    • KRs should be used only in ways that they are intended to be used, that is the source of their power.
  • Representation and reasoning are intertwined
    • 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”.
    • 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
Requirements for KR languages
  • Representation adequacy
    • should to allow for representing all the required knowledge
  • Inferential adequacy
    • should allow inferring new knowledge
  • Inferential efficiency
    • inferences should be efficient
  • Clear syntax and semantics
    • unambiguous and well-defined syntax and semantics
  • Naturalness
    • easy to read and use
some knowledge representation formalisms
Some Knowledge Representation Formalisms
  • Production systems, expert systems
  • Semantic networks
  • Frames
  • Case-based reasoning
  • Biologically inspired approaches
    • neural networks
    • genetic algorithms