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Expert Systems ES Artificial Intelligence AI

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Title: Expert Systems ES Artificial Intelligence AI


1
Expert Systems - ESArtificial Intelligence - AI
  • Mikulá Popper
  • NCKU, Tainan, 2003-2004

2
PROLOGUE
Physics is facing the question What are the
qualities and characteristics of physical objects
of our universe? Biology is facing the
question What is the essence of the verity that
some physical objects are alive? Artificial
Intelligence is facing the question What is the
information-processual essence of systems capable
to rise questions as those?
3
Artificial Intelligence
  • games (chess), cross-words, crypto-arithmetic
    tasks,...
  • algebraic computation, geometric problems,
    calculus (e.g. differentiation, integration),
    solving mathematical tasks formulated in free
    texts, ...
  • interpretation assigning meaning - of natural
    language texts, translations of such texts from
    one to another natural language domain, story
    analysis and production, ...
  • active computer vision, recognition and
    understanding of scenes, situations,
  • cognitive - intelligent - robots,
  • diagnostics interpretation of observable (not
    wishful) features, events, behaviors, i.e.
    identification directly not visible cause based
    on (actively) perceivable phenomena,
  • planning, design, construction, ...
  • learning, adaptation, ...
  • .....

4
AI
THEORY AND PRACTICE OF BUILDING COMPUTER PROGRAMS
THAT PERFORM INTERESTING AND USEFUL
TASKS TECHNIQUES, ALGORITHMS, AND ANALYTICAL
TOOLS THAT ARE USEFUL IN BUILDING SOPHISTICATED
EVEN ITELLIGENT COMPUTER PROGRAMS DESIGN
PROGRAMS THAT RESPOND FLEXIBLY IN SITUATIONS THAT
WERE NOT SPECIALLY ANTICIPATED BY A
PROGRAMMER CONSTRUCT PROGRAMS THAT YIELDS
PERFORMANCE THAT IF PRODUCED BY A HUMAN OR AN
ANIMAL WOULD BE CONSIDERED MANIFISTATIONS OF
INTELLIGECE (OF CERTAING KIND)
5
AI
FOR MANY TASKS CONSIDERED TO BE DIFFICULT - IT IS
EASY TO DEVELOP PROGRAMS MAKING COMPUTERS TO
SOLVE THEM AI programs schedule airlines,
control factories and nuclear power plants,
diagnose problems in complicated electronic
devices or in human body, and perform quite
usefully when translating texts from one into
other natural language, ... OTHER TASKS
CONSIDERED THAT ARE TAKEN FOR GRANTED e.g.
MUNDANE IN THEIR NATURE OR EASY TO BE SOLVED BY
EMPLOYING ONLY COMMONSENSE REQUIRE PROGRAMS
WHOSE DESIGN IS QUITE OR EVEN EXTREMELY
DIFFICULT E.g. programs capable to recognize
faces or control an automatic vacuum cleaner
without hurting pets, destroying furniture, or
distinguishing between a piece of crystal sugar
and a diamond ring.
6
Several attempts are known aiming at
replacing intuitive understanding of Artificial
Intelligence by solid logicaly and/or
mathematicaly acceptable scientific
specification/definition.
  • TURING TEST ? was the first and is the most
    famous attempt by Allan Turing (1950)
  • Another famous one is the Searl's
  • MISTERY OF THE CHINESE ROOM ?
  • No of them is considered as fully satisfying

7
DENDRAL - a practical example Feigenbaum,
Buchanan, Lederberg, 1969
  • A sample is analyzed by a mas spectrograph to
    reveal its che-mical structure i.e. its
    struc-tural formula based on its sum-med
    molecular formula (e.g. C8H16NO2). The resulting
    spec-trogram provides information on the relative
    frequency of molecule sub-structures occur-rence
    having diverse mass-charge ratio.

A respective process, if naive methods are
employed, might be very much similar to the case
of crypto-arithmetic problem. It is so because
the number of all possible mechanical trials
might be in order of hundreds of thousands.
8
  • Initially DENDRAL employed such a naive
    principle of infering structural formulas from
    summed mole-cular formula based on acquired mass
    spektrogram as follows
  • Generate all possible chemical structures being
    consistent with the given summed molecular
    formula.
  • To each generated chemical structure assign
    either a stored or just constructed corresponding
    mass spectrogram of a particular shape.
  • By applying a proper method of pattern matching
    select from all so assigned spectrograms the one
    with the best match against the acquired
    spectrogram.
  • The structural chemical formula related to the so
    identified spectrogram is assumed to be the one
    being searched for the analysed specimen.

9
  • Such an approach is facing a
  • serious problem
  • HIGH COMPUTATIOAL
  • COMPLEXITY!
  • It is so because to each individual
  • summed molecular formula,
  • even in case of not large molecule,
  • might be assigned an astonishing large number of
  • possible spectrograms.
  • Therefore an exhaustive sequence of matching
    trials is not feasible.

10
DENDRAL - a practical example Feigenbaum,
Buchanan, Lederberg, 1969
  • The PROJECT team recog-nized that a naive
    mechanistic approach is not acceptable. They
    realized that specialists in organic chemistry do
    not employ pure mechanistic ap-proach, instead
    they apply available knowledge to elimi-nate
    variants out of question and to hypothesise
    rationally preferable molecular structures to be
    tested first. In this way they minimize the
    number of inevitably exhaustive (mecha-nical)
    reviewing of possibili-ties.

11
SIGNIFICANT OUTCOME OF THE DENDRAL PROJECT?
DISCOVERING POSITION OF EXPLICITKNOWLEDGE
REPRESENTATION
12
  • The new sophisticated approach was based on
    employment a lot of explicitely formulated
    knowledge regarding diverse possible spectrogram
    shapes and structures corresponding to existent
    chemical materials analyzed by a mass
    spectrograph.
  • Illustration
  • A peak (high occurence frequency) in the
    position of mass m15 in the molecule spectrogram
    indicates presence of the metyl component (CH3)
    in it.

13
SUCH NOTIONS LEAD TO REPLACEMENT OF EXHAUSTIVE
OR STOCHASTIC BLIND SEARCH BY KNOWLEDGEABLE
GOAL-ORIENTED PROCESSES SEARCH FIRST FOR WELL
KNOWN AND FREQUENTLY OCCURING (TYPICAL) PEAK
GROUPINGS IN MASS SPECTROGRAMS, FOCUSE ATTENTION
TO SUB-STRUCTURES OF MOLECULES WITH A GIVEN
SUMMED FORMULA WHICH DO EXIST IN REALITY AND
MIGHT BE ANALYZED BY A MASS SPECTROMETRY
Illustration If it is realistic to assume that
in the analysed molecule a ketonic subgroup
(CO) might occure, then in the course of A
spectrogram analysis preference is to be given to
confirm or exclude it.
14
(CO) For that the following (production) rule
is applied IF in the mass spectrogram do exist
two positions x1 and x2 in which peaks occure
for which x1 x2 M 28 (M stands for the
molecule mass) AND in the position x1 - 28 is a
high peak, AND in the position x2 - 28 is a high
peak, AND at least in one from the positions x1,
x2 there is a high peak, THEN The molecule
contains the ketonic sub-group
15
Recognition of some particular elements and their
occurrence in the molecule structure leads to
narrowing of further possible alternatives in the
consecutive analysis and thus the problem-solving
becomes (more) feasible. This verity influenced
the previous approach in solving problems by
DENDRAL by symbolic representation of rules
corresponding to SPECIFIC KNOWLEDGE a practical
and proficient system emerged ALL RELEVANT
THEORETICAL KNOWLEDGE NEEDED FOR SOLVING
CONCERNED PROBLEMS HAVE BEEN TRANSFORMED FROM
THEIR GENERAL FORM CORREPONDING TO THE MASS
SPECTROGRAMS THE SO CALLED FIRST PRINCIPLE')
TO EFFICIENT SPECIFIC FORMS (A KIND OF
COOC/BOOK). (Feigenbaum et al. 1971)
16
THE LESSON GIVEN BY THE ILLUSTRATION IS
IMPORTANT INFORMED - KNOWLEDGEABLE SEARCH
PROCESSES HAVE IN ARTIFICIAL INTELLIGENCE PROGRAMM
ING A PARAMOUNT IMPORTANCE
17
THE VALIDITY OF THIS STATMENT REGARDS
PARTICULARLY THE SYMBOLIC DOMAIN OF ARTIFICIAL
INTELLIGENCE THE SYMBOLIC DOMAIN
REFLECT RATIONAL DELIBERATIVE MENTAL
ACTIVITIES IN THE REALM OF AWARENESS/CONSCIOUSNES
WHEN A HUMAN BEING EMPLOYES HIS KNOWLEDGE ADN
EVEN HIS/HER BELIEFS, STANDS, CONVICTIONS HE/SHE
IS CAPABLE TO EXPRESS IT EXPLICITELY
DENDRAL IS WELL ILLUSTRATING THIS CASE
18
Human beings are successful also in other kinds
of mental activities They are capable to learn
(E.G. THE MOTHER TONGUE), recognize, understand,
see, smell, walk (in streets), climb mountains,
ride cars and horses, play violine, hunt animals,
play tennis, cooperate in footbol or basketbol,
and perform countless other activities controlled
by their brain, however without capacity to
explicate how they are doing what they are doing

Most of these activities are classified
as Sub-symbolic The corresponding processes in
ai Are based on neurocomputations
19
There are nonethless another ai domains concerned
with different approaches, they employ other
computational methods appropriate to
tasks/problems having other than symbolic or
subsymbolic nature
20
SEVERAL AI APPLICATIONS COMBINE SYMBOLIC AND
SUBSYMBOLIC COMPUTATIONS NEWLY DEVISED METHODS OF
REVEALING ANALOGIES ARE CONVINCING ILLUSTRATION
OF THAT
COOPERATION (NOT ONLY IN COLLECTIVE
SPORTS) GENERALLY IN ANY POPULATION OF WHATEVER
ENTITIES (HUMAN BEINGS, ANIMALS, INSECTS ANTS,
BEES?, CEREBRAL DOMAINS, EXTREMITY JOINTS,
ETC.) LIEDS TO DISTRIBUTED ARTIFICIAL
INTELLIGENCE - DAI ALSO COOPERATING MULTIAGENT
SYSTEMS
ANOTHER AI DOMAIN IS CONCERNED WITH EVOLUTION OF
WHATEVER ENTITIES THE EMPLOYED PRINCIPLE IS
GROUNDED IN GENETIC ALGORITHMS
21
PRINCIPLES EMPLOYED IN ARTIFICIAL
INTELLIGENCE SYMBOLIC KNOWLEDGE REPRESENTATION
AND UTILISATION - SYMBOLIC COMPUTATION SUB-SYMBOLI
C KNOWLEDGE REPRESENTATION BY NEURAL NETWORKS AND
NEURO-COMPUTATION COMBINED SYMBOLIC AND
SUB-SYMBOLIC (NOT NECESSARILY NEURO)
COMPUTATION DISTRIBUTED COMPUTATION, MULTI-AGENT
COMPUTATION EVOLUTION MODELLING GENETIC
ALGORITHMS
22
DENDRAL became a source of generalized
knowledge Efficient solving tasks/problems that
by their formulation do not imply the sequence
of solving steps is based on meaningful
application of a sufficient extent general and
professionally specific knowledge. KNOWLEDGE IS
THE GROUND WHICH MAKE POSSIBLE TO EMPLOY BY
STRATEGIES CONTROLLED EXPLORATIONS OF THE PROBLEM
SPACE FOR EFFICIENT PRODUCTION OF CORRECT PROBLEM
SOLVING PROCEDURES
23
EXPERT SYSTEMS PREVAILINGLY BELONG TO THE DOMAIN
OF SYMBOLIC KNOWLEDGE REPRESENTATION AND
SYMBOLIC COMPUTATION
24
Expert systemsare informed and knowledgable
  • naive noninformed blind procedures do not
    ma-nifest any intelligence, they are inefficient
    and have unacceptable computational complexity
    rarely applicable even when solving simple
    nontrivial tasks
  • universal (very general) processes are
    insufficiently informed, thus weak (e.g. laws of
    gravity)
  • informed processes employ prevailingly specific
    knowledge which are means for realizing specific
    goal-oriented problem-solving processes!

25
EXPERT SYSTEMS ARE KNOWLEDGE INTENSIVE SOFTWARE
PRODUCTS EMPLOYING EXPLICITELY EXPRESSED
SYMBOLICALLY REPRESENTED PROFESSIONAL
KNOWLEDGE IN COURSE OF SOLVING TASKS/PROBLEMS
26
Expert systems background
  • artificial intelligence, mathematical (symbolic)
    logic, theoretical practical informatics,
    database systems, symbolic (and subsymbolic)
    representation, asociative (semantic) networks,
    psychology cognitive science, mathematics
    (including probability, graph theory, fuzzy sets)
  • Programing tools KRL, FRL, Prolog, LISP, Clips,
    Java, several ES development tools (e.g.
    NexpertObject, Kappa, ART-IM, and many more)

27
Expert systems attributes - 1
  • Applications solving problems with unknown
    classic algorithmic procedures (ill-formed or
    structured or incompletely specified/informed
    problems)
  • Problem-solving productive non-deterministic
    procedures employing cognitive strategies that
    evenso do not guarantee finding a solution
    frequently make possible to infer required and
    proper results, though not seldom of non
    categorial and noneeqiuvocal nature being
    influenced by diverse uncertaintie
  • Functional characteristics they are embodying
    opportunis-tic, (non-categorical, qualitative,
    uncertain, fuzzy, default) data driven search
    processes based on symbolic represen-tation of
    knowledge from the domain in which the
    consider-ed problem originates

28
Expert system attributes - 2
  • Dynamic control relations among system
    components opportunistic data sharing, data
    exchange, and control transfer among system
    components
  • Methodic of the system design, development, and
    mainte-nance autonomous development and
    modifications of individual functional system
    components, partial and gradual (step-wise)
    design, development, and modification of the
    knowledge base (the data structure comprising the
    symbols representing knowledge and strategies of
    its exploitation)
  • Employment characteristics mostly on-line
    interaction with its user and/or the environment
    in which it operates, less frequently incoming
    data assessment and interpretation in the
    background when performing in interactive
    regimen with a user in many cases it is required
    that the system is capable to provide on demand
    explanation of its activities and inferred
    results, in some cases even on demand or
    automatic adjustment of its activities according
    to the needs, constraints, and user skills

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ES (re)productive processes
  • In case of well-formed tasks/problems the
    problem-solving process is based on reproduction
    of an in advance given algorithm, that is
    suggested by the problem formulation. The
    algorithm is an embodiment of a solving procedure
    a beforehand given sequence of steps. The
    implied process is considered to be of mechanical
    nature.
  • In case of ill-formed tasks/problems that do not
    suggest any particular problem-solving process,
    i.e. no specific algorithm is available, the
    solving process is based on
  • search algorithms when diverse operations
    (solving steps) are searched for, applied,
    tested, and when (currently) applicable, or at
    least there is no reason to exclude them, then as
    potentially proper ones they are sequenced
    (chained together),
  • if the operations prove to be not applicable,
    then they are revised and replaced by other
    operation or even with their sequence.
  • THIS IS THE CASE OF A PRODUCTIVE PROCESS.

30
ES productive processes
  • Productive (cognitive, intelligent)
    problem-solving processes - in contrast with
    those having random or exhaustive nature employ
    knowledgeable sets of both, general and specific
    strategies, that mini-mize the computation
    complexity corresponding to problems at hand.
  • This nature of productive processes makes us to
    perceive expert systems as a software system
    embo-dying knowledgeable strategies of problem
    space explorations aiming at disclosing correct,
    efficent and applicable problem-solving processes
    yielding the needed solutions.

31
ES illustrtion of knowledege application
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GreaterThan GT LargeVessel LV SmallVessel SM
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A non-monotonous process identification of the
same or possibly related lexica and syntactic
features in formulas, e.g. occurrence of the
same predicate symbols, variable symbols, or
predicates with the same arity
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GreaterThan GT LargeVessel LV SmallVessel SV
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GreaterThan GT LargeVessel LV SmallVessel SV
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GreaterThan GT LargeVessel LV SmallVessel SV
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nm (jump)
n (cycle) f(n)
NIL (end of the process)
n1 (implicit control)
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Zbiehanie deklaratívneho programu je riadené (vo
vobecnosti) netriviálnou procedúrou. Na jej
priblíenie pouijeme túto symboliku sk stav
(stuácia) rieenia problému, g - zobrazenie stavu
na mnoinu aplikovatelných (prípustných) operácií
ok,1 t.j. situacno-akcných pravidiel2, f
funkcia výberu vhodného operátora (pravidla,
povelu, intrukcie) - ktorá vo veobecnosti môe
byt pomerne netriviálna, n poradové císlo
vybratého operátora, ktoré pri ukoncení cinnosti
je nahradené prísluným symbolom NIL. Teda
riadiaca procedúra zodpovedá zobrazeniu a
následnej aplikácii výberovej funkcie. Mono to
vyjadrit v nasledujúcej podobe
?n f(g(sk)) f(ok)
?
?NIL Výberová funkcia f, v závislosti na
dômyselnosti jej realizácie, môe na jednej
strane zodpovedat triviálnym, na druhej strane a
velmi zloitým a sofistikovaným, heuristikami
podmienovaným, procesom. Nasledujú niektoré
monosti mechanický výber najmenieho, ci
najväcieho n, náhodilý výber operácie
(akcie), výber operácie (akcie), ktorá bola
posledne preruená, teda nedokoncená, je bud zo
vetkých prípustných najveobecnejia alebo
najpecializovanejia, posledne vykonanú
najlepie doplnuje, alebo je práve jej opakom, sa
posledne vykonanej a iba ciastocne úspenej
najviac podobá, má pre uskutocnenie k dispozícii
najviac údajov alebo najlepie údaje
(spolahlivost, presnost, kategorickost,
pecifickost, senzitívnost, diskriminacná
úcinnost a pod.), výber operácie, ktorá je bud
najcastejie alebo najzriedkavejie pouívaná,
spôsobí aktiváciu bud najväcieho alebo
najmenieho poctu nadväzujúcich akcií, spôsobí
aktiváciu najlacnejích (napr. v zmysle
výpoctovej zloitosti, nárokov na doplnenie
chýbajúcich údajov a pod.) nadväzujúcich
akcií, má potenciál získat najviac nových alebo
najdiferencujúcejich informácií, sa v
analogickej situácii najcastejie
osvedcovala, vzhladom na dané kritéria, pokial
ich splnuje, zabezpecuje najrýchlejie
dosiahnutie cielového stavu, a dalie. 1
Mnoina aplikovatelných operácií implikuje vznik
nedeterminizmu ! 2 V stuáciach, pre ktoré
absentujú potrebné poznatky, táto mnoina môe
byt aj prázdna. Taký prípad vyaduje prostriedok
oetrenia vzniknutého stavu.
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PROCEDURAL AND DECLARATIVE PROGRAMS DIFFERENCE
BETWEEN CONTROL MECHANISMS
nm (jump)
n (cycle) f(n)
NIL (end of the process)
n1 (implicit control)
DETERMINISTIC
n f(g(sk))
f(ok)
NIL
NONDETERMINISTIC
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General schema of an EXPERT SYSTEM
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  • PRACTICING
  • Design a program capable to
  • figure out a triangle surface for any valid
    combination of input parameters, e.g. AB edge and
    a and ß angles
  • solve simple crypto-arithmetic problems

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  • THE END TODAY
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