University of Surrey
School of ECM
University of Surrey
Guildford, Surrey

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PROSPECTOR: Operational details

PROSPECTOR: Knowledge Base

PROSPECTOR's Inference Mechanism

PROSPECTOR: Conclusions



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"

                                                     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,
                                                     California, USA

                                                     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 Artificial Intelligence".
                                                     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

PROSPECTOR: Operational details

Characterisitics of a particular 'prospect'(exploration site)
volunteered by expert
(e.g.geologic setting, structural controls, and kinds of rocks minerals, and
alteration products present or suspected)

PROSPECTOR compares observations with stored models of
ore deposits

PROSPECTOR notes similarities, differences and missing

(POSPECTOR asks for additional information if neccessary)

PROSPECTOR assesses the mineral potential of the prospect


system has been kept domain independent

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

the input data are assumed to be incomplete and uncertain



PROSPECTOR: Operational details

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

SEMANTIC NETWORKS to organize the domain
knowledge and backward chaining inference strategy

PROSPECTORS' Knowledge Base:

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

PROSPECTOR system contains rules linking observed evidence, 'E'. of the particular (geological) findings with 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.

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, that humans represent the real-world through a series of associations. More precisely a semantic network is defined as a type of knowledge representation that formalises objects and values as nodes and connects the nodes with arcs or links that indicate the relationships between the various nodes: A data structure for representing declarative knowledge. It can be argued that the nodes can also represent concepts, and the arcs the relations between concepts, thereby forming semantic networks.Quillian has pointed out the "type-token" distinction. This may be related to the generic/specific relationship.

PROSPECTOR's Inference Mechanism

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.


                                                            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 )

                                        LS is used when the evidence is known to exist.

                                        Probabilities are provided subjectively by the expert

PROSPECTOR's Inference Mechanism

Probablistic Reasoning


When the evidence is known to NOT exist
  P(h | not.e) = LN x P(e)
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 to within 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 if the 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 time
therefore, 1 - P(h) can be viewed as an estimate of the expert's disbelief regarding the truth of h.

Measure of Belief: If P[h ¦ e] is greater than P(h), the observation of 'e' increases the expert'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 the expert'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] = --------------------------

Belief and disbelief correspond to the intuitive concepts of confirmation and disconfirmation

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] = 0
        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') representing certainity of the inference contained in the rule: MYCIN combines several sources of inconclusive information 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' rule is 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 particular set of circumstances. Probablistic information is not neccessary.