University of Surrey
School of ECM
University of Surrey
Guildford, Surrey
GU2 5XH, UK
Tel: +44 (0)1483 259823
Fax: +44 (0)1483 876051
 
 
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,
                                                     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 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
information

(POSPECTOR asks for additional information if neccessary)

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 for specific ore deposits

• the input data are assumed to be incomplete and uncertain

 

PROSPECTOR At Work
 
 







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
 
 
PROSPECTOR uses PRODUCTION RULES and
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.
 

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
 
 

PROSPECTOR's Inference Mechanism

Probablistic Reasoning

Definition

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

PROBABLISTIC REASONING: MYCIN, XCON and PROSPECTOR

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] = --------------------------
                            P(h)

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

(evidence is independent of hypothesis)
 
 
PROBABLISTIC REASONING: MYCIN, XCON and PROSPECTOR

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.