expert systems case studies

School of ECMUniversity of SurreyGuildford, SurreyGU2 5XH, UK

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Introduction

PROSPECTOR: Operational details

PROSPECTOR: Knowledge Base

PROSPECTOR's Inference Mechanism

PROSPECTOR: Conclusions

PROBABLISTIC REASONING: MYCIN, XCON and PROSPECTOR

PROSPECTOR: An Introduction

Problem domain: • Evaluation of the mineral potential of a geological site or region

• Multi-disciplinary decision making: PROSPECTOR deals with geologic setting, structural controls, and kind of rocks, minerals, and alteration products present or suspected

Target Users: Exploration geologist who is in the early part of investigating an exploration site or "prospect"

Originators R. Duda, P. E.Hart, N.J. Nilsson, R. Reboh, J. Slocum, and G. Sutherland and John Gasching (1974-1983) Artificial Intelligence Center, Stanford Research Institute (SRI) International Menlo Park,

Expert Systems Case Studies:Prospector http://www.computing.surrey.ac.uk/AI/PROFILE/prospector.html#Introdu...

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California, USA

References: Waterman A., Donald., (1986), "A Guide to Expert Systems". Reading,Mass (USA). Addison-Wesley Publishing Company. pp 49-60 Barr, Aaron & Feigenbaum, Edward., (1982) "The Handbook of ArtificialIntelligence". Reading, Mass (USA). Addison-Wesley Publishing Company. pp 155-162

PROSPECTOR: An Introduction

• consultation system to assist geologists working in mineral exploration

• developed by Hart and Duda of SRI International

• attempts to represent the knowledge and reasoning processes of experts in the geological domain

• intended user is an exploration geologist in the early stages of investigating a possible drilling site


Characterisitics of a particular 'prospect'(exploration site)volunteered by expert

(e.g.geologic setting, structural controls, and kinds of rocks minerals, andalteration products present or suspected)

PROSPECTOR compares observations with stored models ofore deposits

PROSPECTOR notes similarities, differences and missinginformation

(POSPECTOR asks for additional information if neccessary)


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PROSPECTOR assesses the mineral potential of the prospect

PROSPECTOR

• system has been kept domain independent

• it matches data from a site against models describing regional and local characteristics favourable forspecific ore deposits

• the input data are assumed to be incomplete and uncertain

PROSPECTOR At Work


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PROSPECTOR performs a consultation to determine such things as

• which model best fits the data

• where the most favourable drilling sites are located

• what additional data would be most helpful in reaching firmer conclusions

• what is the basis for these conclusions and recommendations

PROSPECTOR: Knowledge Base

The Knowledge Base (K.B.) is divided into two parts

• General Purpose K.B. contains background information useful for several applications and situations e.g. general classification tree

• Special Purpose K.B.

contains information relevent to a specific part of the domain, primarily in the form of inference networks

PROSPECTOR uses PRODUCTION RULES andSEMANTIC NETWORKS to organize the domain

knowledge and backward chaining inference strategy

PROSPECTORS' Knowledge Base:

The Representation SchemeThe knowledge representation scheme used by the developer's of PROSPECTOR is called 'the inferencenetwork': a network of connections between evidence and hypotheses or a network of nodes (assertions)andarcs (links)


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PROSPECTOR system contains rules linking observed evidence, 'E'. of the particular (geological) findingswith hypotheses, 'H', implied by the evidence:

If E then H (to degree) LS, LN;

LS and LN are prestored (ranging from +5 to -5) and do not change during the execution of the program.Also, each piece of evidence (E1,E2, E3..) and hypotheses (H1...) has a probability assigned to it (P1,P2..)whichmay change during execution according to Baye's Theorem.


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PROSPECTOR: Knowledge Base:

Static Data

In addition to the PROSPECTOR rule-base, the system also has a large taxonomic network: A 'hierarchical'data-base containing super- and sub-ordinate relationships between the objects of the domain.

PROSPECTOR Knowledge Base

Semantic networks: Quillian (1966) introduced the idea of semantic networks based on the so-called"associative memory model": the notion that human memory is organized on the basis of association, thathumans represent the real-world through a series of associations. More precisely a semantic network is


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defined as a type of knowledge representation that formalises objects and values as nodes and connects thenodes with arcs or links that indicate the relationships between the various nodes: A data structure forrepresenting declarative knowledge. It can be argued that the nodes can also represent concepts, and the arcsthe relations between concepts, thereby forming semantic networks.Quillian has pointed out the "type-token"distinction. This may be related to the generic/specific relationship.


Probablistic Reasoning

To deal with uncertainty PROSPECTOR uses

• subjective probability theory (including Bayes' theorem.) supplemented by Certainty Factors (MYCIN) and fuzzy sets.

A form of Bayes' theorem called "odds-liklihood"is used in PROSPECTOR.

ODDS = PROBABILITY (1-PROBABILITY)

Definition

P(h) = LS x P(h)

P(h) = prior odds on the hypothesis h

P(h|e) = posterior odds on hypothesis (new odds given evidence)

LS = sufficiency measure of the rule

LS = P(e|h) ( = liklehood ratio ) P(e|not.h)

• LS is used when the evidence is known to exist.

• Probabilities are provided subjectively by the expert


Probablistic Reasoning

Definition

When the evidence is known to NOT exist

P(h | not.e) = LN x P(e)


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LN = measure of necessity

LN = P(not e|h) P(not e| not.h)

Again the probabilities are given subjectively by the domain expert.

PROSPECTOR: Conclusions

Points to note about the PROSPECTOR system

• the conclusions drawn by the PROSPECTOR system match those of the expert who designed the system towithin 7% on a scale used to represent the validity of the conclusions

• work on the system illustrated the importance of accommodating the special characteristics of a domain ifthe system is intended for practical use - all domains have their own peculiarities in how decisions are made


Evidential Strength Model and Certainty: MYCIN approach

• According to the subjective probability theory:

expert's personal probability, P(h), reflects his/her belief in h at any given timetherefore, 1 - P(h) can be viewed as an estimate of the expert's disbelief regarding thetruth of h.

• Measure of Belief: If P[h ¦ e] is greater than P(h), the observation of 'e' increases theexpert's belief in 'h' while decreasing disbelief in h. Proportionate decrease in disbelief (alternatively, the measure of belief increment) due to the observation 'e' is

P(h y e) - P(h)MB[h ,e] = -------------------------- 1 - P(h)

• Measure of Disbelief: If P[h ye] is less than P(h), the observation of 'e' decreases theexpert's belief in 'h' while increasing disbelief in h. Proportionate decrease in belief (alternatively, the measure of disbelief increment) due to observation 'e' is:

P(h ) - P(h y e)MD[h ,e] = -------------------------- P(h)

• Belief and disbelief correspond to the intuitive concepts of confirmation anddisconfirmation


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• Because a given piece of evidence cannot support both belief and disbelief, therefore

if MB[h ,e] > 0 then MD[h ,e] = 0; if MD[h , e] > 0 then MB[h ,e] = 0and if P(h ¦ e) = P(h) then MB[h , e] = MD[h , e] = 0

(evidence is independent of hypothesis)


MYCIN: Each rule is associated with a number between 0 and 1 (CF, the 'cretainity factor') representingcertainity of the inference contained in the rule: MYCIN combines several sources of inconclusiveinformation to form a conclusion of which it may be almost certain. Ad-hoc appraoch to probability

PROSPECTOR: Confidence measures (LS,LN)are interpreted precisely as as probabilities and Bayes' ruleis used as the basis of inference procedure.

XCON: In XCON's task domain it is possible to state exactly the correct thing to be done in each particularset of circumstances. Probablistic information is not neccessary.


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