Aspects of Artificial Intelligence

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Aspects of Artificial Intelligence

• Paul Lewis

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What is AI?

• One definition
• -the application of computers to areas normally
  regarded as requiring human intelligence

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Some dictionary games

• Intelligence quickness of understanding
• Understanding ability to perceive the meaning
• Meaning that which is intended to be conveyed
• Intelligence is quickness of ability to perceive
  that which is intended to be conveyed !!

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Are these intelligent?

• a simple calculator
• a program to find a path through a network
• a system which 'sees' a component and controls a
  robot arm to build it into an assembly
• Yes/No--- some say AI is the study of how to
  make computers do things which at the moment
  humans are better!!!!!
• If a computer can do it, it isn't AI

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Another definition

• AI is the study of computations that make it
  possible to
• - perceive
• - reason
• - act

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AI

• AI is concerned with modelling both the
  activities of the brain and the functions of
  other parts of human beings
• brain -gt recall and reasoning
• ears -gt speech recognition
• mouth -gt speech synthesis
• eyes-gt vision
• arms and legs -gt robotics

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AI is multidisciplinary!Interested parties

• Computer scientists, mathematicians and
  electronic engineers - to build more useful
  systems
• Psychologists, neurophysiologists and
  philosophers - to understand (using computer
  models) the principles which make intelligence
  possible
• NB AI is still very much a research area.

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Strong vs Weak AI

• Will it ever be possible to build a complete
  computer model of the human brain (mind) ?
• Would such a model actually understand the
  meaning of anything? Would it be conscious and
  self conscious or are these mental qualities
  peculiar to humans?
• The strong view says
• the brain is a very complicated
  information processing machine . Consciousness,
  understanding etc are by-products of the
  complicated symbol manipulation. We will be able
  to model it.
• The weak view says
• it will not be possible to model all the
  properties of the human brain

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Central Issues in AI

• Knowledge representation
• Reasoning and control
• Learning (Knowledge acquisition)
• Handling uncertainty
• Forming plans
• Vision (object recognition, motion and video
  analysis, scene understanding)
• Speech (recognition, synthesis, story
  comprehension)

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Knowledge Representation

• Symbolic verses sub symbolic
• cf your brain.
• Conscious processing tends to be symbolic
• Low level activity is electrochemical
• sub-symbolic

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Rules for knowledge Representation

• RULES
• IF a person P complains of symptom S
• AND drug D relieves symptom S
• THEN person P should take drug D
• FACTS
• GLOBAL
• aspirin relieves headache
• LOCAL
• john complains of headache

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REASONING with RULES

• involves forward and backward chaining through
  rules
• forward data driven
• backward goal driven

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Knowledge Rep Semantic Nets
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Semantic Nets
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Knowledge Rep Semantic Nets

• nodes are objects or concepts
• arcs are relations
• Reasoning involves graph search and graph
  sub-graph matching

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Typical AI Applications

• Expert Systems
• medical diagnosis
• legal systems
• financial advisors
• fault diagnosis
• Intelligent software agents
• personalised information retrieval agents
• financial agents (automated share
  dealing)
• Robotics
• automated assembly
• autonomous vehicles

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Typical AI Applications (cont)

• Speech systems
• telephone answering systems
• (use speech recognition and speech
  synthesis)
• Vision systems
• industrial inspection
• robot guidance
• medical image analysis
• remotely sensed image analysis

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PROLOG

• A Symbolic Approach to AI
• Paul Lewis

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Logic and Computer-Based Reasoning

• Logic - invented by the Greeks to formalise
  reasoning
• How to spot false arguments
• How to put sound arguments
• How to INFER
• Mathematical Logic
• Propositional Calculus
• A B ? C
• Predicate Calculus
• complains_of(P,S) relieves(D,S)
  unsuitable_for(D,P)
• ?
  should_take(P,D)

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PROLOG

• PROgramming in LOGic
• A declarative programming language
• (cf imperative languages like Basic,
  Pascal, C)
• Based on the predicate calculus
• Developed in about 1970 by Alain Colmerauer
• Uses resolution, a general rule of inference

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Programming in PROLOG

• Involves declaring facts and rules about objects
  and their relationships (predicates) by placing
  them in a computer file (the knowledge base)
• Then asking questions of the knowledge base
  (i.e. asking PROLOG to satisfy goals)
• PROLOG uses its own built in reasoning mechanism
  to do this
• PROLOG programs are backward chaining (goal
  driven) rule-based systems

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Facts and Rules in Prolog

• Facts and rules are expressed as clauses in
  Prolog.
• plays(john,football).
• designed to mean john plays football
• The interpretation is defined by the writer.
• parent(jim, jack).
• Jim is the parent of jack
• Constants begin with lowercase, variables with
  upper case.
• Rules have a head and a body e.g head-body. If
  the body can
• be shown to be true, the head is true. body ?
  head.
• older_than(X,Y) -
• age_of(X,AX),
• age_of(Y,AY),
• AX gt AY.

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A small PROLOG Knowledge Base
male(john). / john is male / male(paul). female(
emma). / emma is female / female(cath). likes(p
aul,cycling). / paul likes cycling
/ likes(cath,cycling). likes(john,X) -
female(x),likes(X,cycling). / john likes X if X
is female and X likes cycling /

• ?- consult(knowledgebase).
• yes
• ?- male(john).
• yes
• ?- female(paul).
• no
• ?- likes(paul,Y).
• Ycycling
• more(y/n) y
• no
• ?- likes(john,Something).
• Somethingcath
• more(y/n) y
• no
• ?-

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Recursion in Prolog

• parent(bill,fred).
• / bill is a parent of fred /
• parent(jack,bill).
• parent(jim,jack).
• ancestor(X, Y) - parent(X,Y).
• ancestor(X,Y) - parent(Z,Y),
  ancestor(X,Z).
• --------------------------------------------
  -----------
• ?- ancestor(jim,fred).
• yes
• ?-

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Lists in Prolog

• A list consists of a collection of items.
• john,jack,fred,jim
• A list has a head (first element)
• and a tail (LIST of all the other elements)
• HeadTail
• For list above Headjohn
• Tailjack,fred,jim
• A Prolog rule defining list membership.
• / member(X,L) means X is a member of list L /
• member( X , X _ ).
• member( X , _ T ) - member ( X , T ).

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Slightly Larger Knowledgebase

• / Global Facts /
• / Knowledge Base of Pain Killers /
• relieves(aspirin,headache).
• relieves(wonderdrug,headache).
• relieves(aspirin,toothache).
• relieves(penecillin,pneumonia).
• aggravates(aspirin,asthma).
• / Global Rules /
• should_takePerson,Drug)-
• complains_of(Person,Symptom),
• relieves(Drug,Symptom),
• not(unsuitable_for(Drug,Person)).
• unsuitable_for(Drug,Person)-
• aggravates(Drug,Symptom),
• suffers_from(Person,Symptom).

• ?- consult(filename).
• yes
• ?- should_take(john,D).
• Dwonderdrug
• more(y/n) y
• no
• ?-

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A Typical Expert System