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A Review of Research Topics of the AI Department in Kharkov
(MetaIntelligence Laboratory)
Vagan Terziyan, Helen Kaikova
November 25 - December 5, 1999Vrije University of Amsterdam (Netherlands)
First Joint Research Seminar of AI Departments of Ukraine and Netherlands
Ф илиал-каф едра
Authors
Helen KaikovaVagan Terziyan
Metaintelligence Laboratory
Department of Artificial IntelligenceKharkov State Technical University of Radioelectronics, UKRAINE
[email protected] [email protected]
In cooperation with:
Contents A Metasemantic Network Metasemantic Algebra of Contexts The Law of Semantic Balance Metapetrinets Multidatabase Mining and Ensemble of Classifiers Trends of Uncertainty, Expanding Context and Discovering
Knowledge Recursive Arithmetic Similarity Evaluation in Multiagent Systems On-Line Learning
A Metasemantic Network
Semantic Metanetwork is considered formally as the set of semantic networks, which are put on each other in such a way that links of every previous semantic network are in the same time nodes of the next network
A Semantic Metanetwork
An Example of a Semantic Metanetwork
A''A''
A''
1
3
2
L''1
L''2
A'2
A'3
A'4
A'1
L'3
L'2L'
1
A2
A1
A3
L2L
1
L3
L4
Zero level
First level
Second level
How it Works
• In a Semantic Metanetwork every higher level controls semantic structure of the lower level.
• Simple controlling rules might be, for example, in what contexts certain link of a semantic structure can exist and in what context it should be deleted from the semantic structure.
• Such multilevel network can be used in an adaptive control system which structure is automatically changed following changes in a context of the environment.
• The algebra for reasoning with a semantic metanetwork is also developed.
Published and Further Developed in
Terziyan V., Multilevel Models for Knowledge Bases Control and Their Applications to Automated Information Systems, Doctor of Technical Sciences Degree Thesis, Kharkov State Technical University of Radioelectronics, 1993
Puuronen S., Terziyan V., A Metasemantic Network, In: E. Hyvonen, J. Seppanen and M. Syrjanen (eds.), SteP-92 - New Directions in Artificial Intelligence, Publications of the Finnish AI Society, Otaniemi, Finland, 1992, Vol. 1, pp. 136-143.
A Metasemantic Algebra for Managing Contexts
trueALAP jki ),,(
Semantic predicate describes a piece of knowledge (relation or property) by the expression:
if there is knowledge that a relation with name Lk holds between objects Ai and Aj
A Semantic Predicate
Example of Knowledge
“ B i l l h a t e s p o o r M a r y ” .
A 1 : < B i l l > ; A 2 : < M a r y > - o b j e c t s ;L 1 : < t o h a t e > ; L 2 : < t o b e p o o r > - n a m e s o f r e l a t i o n s ;
MaryhatesBillALAP ),,( 211 - r e l a t i o n ;
pureisMaryALAP ),,( 221 - p r o p e r t y ;
K n o w l e d g e :
trueALAPALAP ),,(),,( 323312
Ai Aj Ai AjLk~Lk
Semantic Operations: Inversion
),~
,(),,( ikjjki ALAPALAP
kk LL ~~
),,(),,( jkijki ALAPALAP
Semantic Operations: Negation
P(<Mary>, <to_love>, <Tom>) = false,
it is the same as:
P(<Mary>, <not_to_love>, <Tom>) = true.
kk LL kk LL~~
As
Aj
Lk
Ai
Ln As
Aj
Lk
Ai
Ln
L Lk n*
Semantic Operations: Composition
),*,(),,(),,( jnkijnsski ALLAPALAPALAP
If it is true: P(<Mary>, <to_be_married_with>, <Tom>) and
P(<Tom>, <to_have_mother>, <Diana>),
then it is also true that:
P(<Mary>, <to_have_mother-in-law>, <Diana>).
Aj Lk
Ai
Ln
AjAiL Lk n
Semantic Operations: Intersection
),,(),,(),,( jnkijnijki ALLAPALAPALAP
<to_give_birth_to> + <to_take_care_of> = <to_be_mother_of>.
Al'
AjLkAi
Ln'
AjAiLkLn'a)
Semantic Operations: Interpretation
Al'
LkAi
Ln'
Ai
LkLn'
b)
),,(
)),,(,(),,(),,('
''''
jLki
jkillnljki
ALAP
ALAPAistALAPALAP
n
level thn of metacont.about knowl....contextabout knowl.knowledge
knowledge dinterprete
Interpreting Knowledge in a Context
A''A''
A''
1
3
2
L''1
L''2
A'2
A'3
A'4
A'1
L'3
L'2L'
1
A2
A1
A3
L2L
1
L3
L4
Zero level
First level
Second level
Example of Interpretation
'3
'2
'1
31)()(*)( 321
AAALLL
AA LLLL
)''1
~*''2
~''2*''1('
3'
2'
1'
2'3
'1
'2
'1 )*
~~*
~**(
321 )*(LLLLLLLLLLLLLLL
The interpreted knowledge about the relation between A1 and A3 taking all contexts and metacontexts into account is as follows:
Decontextualization
knowledge d interpretecontext about knowledge
x L
Suppose that your colleague, whose context you know well, has described you a situation. You use knowledge about context of this person to interpret the “real” situation. Example is more complicated if several persons describe you the same situation. In this case, the context of the situation is the semantic sum over all personal contexts.
Context Recognition
knowledge dinterpreteknowledge xL
Suppose that someone sends you a message describing the situation that you know well. You compare your own knowledge with the knowledge you received. Usually you can derive your opinion about the sender of this letter. Knowledge about the source of the message, you derived, can be considered as certain context in which real situation has been interpreted and this can help you to recognize a source or at least his motivation to change the reality.
tioninterpreta)(
alizationdecontextu)()(nL
mn
mm
Lk
Lx
Lkxk
Lx
LL
LLLL
Lifting (Relative Decontextualization)
This means deriving knowledge interpreted in some context if it is known how this knowledge was interpreted in another context.
Terziyan V., Puuronen S., Reasoning with Multilevel Contexts in Semantic Metanetworks, In: D.M. Gabbay (Ed.), Formal Aspects in Context, Kluwer Academic Publishers, 1999, pp. 173-190.
Published and Further Developed in
Terziyan V., Puuronen S., Multilevel Context Representation Using Semantic Metanetwork, In: Context-97 - Proceedings of International and Interdisciplinary Conference on Modeling and Using Context, Rio de Janeiro, Brazil, Febr. 4-6, 1997, pp. 21-32.
The Law of Semantic Balance
W
A1
A2
A3
A
L1
L2
L3
L4
L5
L6
A4
A5
A6
A8
A7
An Object in Possible World
W
A1
A2
A3
L1
L2
A
W
A
L3
L4
L5
L6
a) b)
A4
A5
A6
A7
A8
Internal and External View of an Object
Internal semantics of object is equal to semantic sum of all chains of semantic relations that start and finish on the shell of this object and pass inside it:
Internal Semantics of an Object
),_,(),_,(
,,,
)_**_(
ki
ji
kjii
APARTHASAPAPARTHASAP
kjkjAAAA OFPARTLPARTHASL
External Semantics of an Object
External semantics of object is equal to internal semantics of the World if consider this object as an Atom in this World (i.e. after removing internal structure of the object from the World):
)()( WorldAE EAtomA
iniexi
External and internal semantics of any object as evolutionary knowledge are equivalent to each other in a limit.
The Law of Semantic Balance
))((lim=))((lim )()(i
tex
ti
tin
tAEAE
nil in
nil in
nil in
nil in
nil
nil
nil
nil
ex
ex
ex
ex
...
Balance
Step 1
Balance
Step 2
Balance
Balance
E
E
E
E
E
E
E
E
in ex
in
in
in
ex
ex
ex
Ein Eex
a)
b)
c)
d)
e)
f)
NIL NIL
The Evolution of Knowledge
Published and Further Developed in
Terziyan V., Multilevel Models for Knowledge Bases Control and Their Applications to Automated Information Systems, Doctor of Technical Sciences Degree Thesis, Kharkov State Technical University of Radioelectronics, 1993
Grebenyuk V., Kaikova H., Terziyan V., Puuronen S., The Law of Semantic Balance and its Use in Modeling Possible Worlds, In: STeP-96 - Genes, Nets and Symbols, Publications of the Finnish AI Society, Vaasa, Finland, 1996, pp. 97-103.
Terziyan V., Puuronen S., Knowledge Acquisition Based on Semantic Balance of Internal and External Knowledge, In: I. Imam, Y.Kondratoff, A. El-Dessouki and A. Moonis (Eds.), Multiple Approaches to Intelligent Systems, Lecture Notes in Artificial Intelligence, Springer-Verlag, V. 1611, 1999, pp. 353-361.
Metapetrinets for Flexible Modelling and Control of Complicated Dynamic Processes
A Metapetrinet
• A metapetrinet is able not only to change the marking of a petrinet but also to reconfigure dynamically its structure
• Each level of the new structure is an ordinary petrinet of some traditional type.
• A basic level petrinet simulates the process of some application.
• The second level, i.e. the metapetrinet, is used to simulate and help controlling the configuration change at the basic level.
How it Works
There is conformity between the places of the second level structure and places or transitions of the basic level structure.
One possible control rule is such that a certain place or transition is removed from the present configuration of the basic level if the corresponding place at the metalevel becomes empty.
If at least one token appears to an empty metalevel place, then the originally defined corresponding basic level place or transition immediately is created back to the configuration
P´1
P2
P1
P4P3
t1
t2
t´3
P´3
t´2P´5
P´4
P´2
t´1
Controllinglevel
Basic level
Example of a Metapetrinet
basic level petrinet attributes
<place> <transition> <link> <token>
controllingeffect
1) removing a place;
2) restoring a place;
3) changing aplace’s capacity;
4) changing aplace’s marking
1) removing atransition;
2) restoring atransition;
3) changingtime settings;
4) changingthe fire rule
1) removing alink;
2) restoring alink;
3) changing alink’s direction;
4) changing alink’s capacity
1) removing atoken;
2) restoring atoken;
3) changing atoken’s color;
4) changing atoken’s place
Controlling Interactions between Metapetrinet’s Levels
Terziyan V., Multilevel Models for Knowledge Bases Control and Their Applications to Automated Information Systems, Doctor of Technical Sciences Degree Thesis, Kharkov State Technical University of Radioelectronics, 1993
Savolainen V., Terziyan V., Metapetrinets for Controlling Complex and Dynamic Processes, International Journal of Information and Management Sciences, V. 10, No. 1, March 1999, pp.13-32.
Published and Further Developed in
Mining Several Databases with an Ensemble of Classifiers
Problem
DB nDB 1
Sample
?
Classifiers
Data Sets
Result
Classifier m
x Classifier 1
DB
?x
Classifier m
Classifier 1
Case ONE:MANY
Dynamic Integration of Classifiers
Final classification is made by weighted voting of classifiers from the ensemble;
Weights of classifiers are recalculated for every new instance;
Weighting is based on predicted errors of the classifiers in the neighborhood area of the instance
Case MANY:ONE
DB 1
?x Classifier
DB n
Integration of Databases
Final classification of an instance is obtained by weighted voting of predictions made by the classifier for every database separately;
Weighting is based on taking the integral of the error function of the classifier across every database
Case MANY:MANY
DB 1
?x
Classifier m
Classifier 1
DB n
Solutions for MANY:MANY
MANY:MANY
DB 1
Classifier m
Classifier 1
DB n
ONE:MANY
Classifier m
Classifier 1
DB
MANY:ONE
DB 1
Classifier
DB n
ONE:ONE
DB
Classifier
Decontextualization of Predictions
Sometimes actual value cannot be predicted as weighted mean of individual predictions of classifiers from the ensemble;
It means that the actual value is outside the area of predictions;
It happens if classifiers are effected by the same type of a context with different power;
It results to a trend among predictions from the less powerful context to the most powerful one;
In this case actual value can be obtained as the result of “decontextualization” of the individual predictions
Neighbor Context Trend
1
2
3
x
prediction in (1,2) neighbor context: prediction in (1,2) neighbor context: ““worse contextworse context””
prediction in (1,2,3) neighbor prediction in (1,2,3) neighbor context: “context: “better contextbetter context””
actual value: “actual value: “ideal contextideal context””y
xi
y(xi)
y+(xi)
y-(xi)
Main Decontextalization Formula
y
Y
y- - prediction in worse context
y+ - prediction in better context
y’ - decontextualized prediction
y - actual value
y’y+y-
’
+
-
’ ’ == -- ··++
-- + + ++ ’ < - ; ’ < ++ < -
Some Notes Dynamic integration of classifiers based on locally adaptive weights
of classifiers allows to handle the case «One Dataset - Many Classifiers»;
Integration of databases based on their integral weights relatively to the classification accuracy allows to handle the case «One Classifier - Many Datasets»;
Successive or parallel application of the two abowe algorithms allows a variety of solutions for the case «Many Classifiers - Many Datasets»;
Decontextualization as the opposite to weighted voting way of integration of classifiers allows to handle context of classification in the case of a trend
Published in
Puuronen S., Terziyan V., Logvinovsky A., Mining Several Data Bases with an Ensemble of Classifiers, In: T. Bench-Capon, G. Soda and M. Tjoa (Eds.), Database and Expert Systems Applications, Lecture Notes in Computer Science, Springer-Verlag , V. 1677, 1999, pp. 882-891.
Other Related Publications
Terziyan V., Tsymbal A., Puuronen S., The Decision Support System for Telemedicine Based on Multiple Expertise, International Journal of Medical Informatics, Elsevier, V. 49, No.2, 1998, pp. 217-229.
Tsymbal A., Puuronen S., Terziyan V., Arbiter Meta-Learning with Dynamic Selection of Classifiers and its Experimental Investigation, In: J. Eder, I. Rozman, and T. Welzer (Eds.), Advances in Databases and Information Systems, Lecture Notes in Computer Science, Springer-Verlag, Vol. 1691, 1999, pp. 205-217.
Skrypnik I., Terziyan V., Puuronen S., Tsymbal A., Learning Feature Selection for Medical Databases, In: Proceedings of the 12th IEEE Symposium on Computer-Based Medical Systems CBMS'99, Stamford, CT, USA, June 1999, IEEE CS Press, pp.53-58.
Puuronen S., Terziyan V., Tsymbal A., A Dynamic Integration Algorithm for an Ensemble of Classifiers, In: Zbigniew W. Ras, Andrzej Skowron (Eds.), Foundations of Intelligent Systems: 11th International Symposium ISMIS'99, Warsaw, Poland, June 1999, Lecture Notes in Artificial Intelligence, V. 1609, Springer-Verlag, pp. 592-600.
An Interval Approach to Discover Knowledge from Multiple Fuzzy Estimations
The Problem of Interval Estimation
Measurements (as well as expert opinions) are not absolutely accurate.
The measurement result is expected to lie in the interval around the actual value.
The inaccuracy leads to the need to estimate the resulting inaccuracy of data processing.
When experts are used to estimate the value of some parameter, intervals are commonly used to describe degrees of belief.
Noise of an Interval Estimation
In many real life cases there is also some noise which does not allow direct measurement of parameters.
The noise can be considered as an undesirable effect (context) to the evaluation of a parameter.
Different measurement instruments as well as different experts possess different resistance against the influence of noise.
Using measurements from several different instruments as well as estimations from multiple experts we try to discover the effect caused by noise and thus be able to derive the decontextualized measurement result.
Decontextualization of Noise in Pattern Recognition with Multiple Estimations
Dec
onte
xtu
aliz
atio
n
pattern
noiseestimations
recognized pattern
1
2
3
4
1
2
3
4
result
Basic Assumption
The estimation of some parameter x given by more accurate knowledge source (i.e. source guarantees smaller upper bound of measurement error) is supposed to be closer to the actual value of parameter x (i.e. source is more resistant against a noise of estimation).
The assumption allows us to derive different trends in cases when there are multiple estimations that result to shorter estimation intervals.
a i
b i
a j
b j
a r e s
b r e s
a , b
uu i u j
u u
u ui j
i j
b u ( )
a f u ( )
Basic Idea of Decontextualization
a2 b2
a3 b3
ares bres
a1 b1
x0 1 2 3 4 5 6 7 8 9 10 11 12 13
An Example
Some Notes
If you have several opinions (estimations, recognition results, solutions etc.) with different value of uncertainty you can select the most precise one,
however it seems more reasonable to order opinions from
the worst to the best one and try to recognize a trend of uncertainty which helps you to derive opinion more precise than the best one.
Application of the Trend of Uncertainty to Image Restoration
Published and Further Developed in
Terziyan V., Puuronen S., Kaikova H., Interval-Based Parameter Recognition with the Trends in Multiple Estimations, Pattern Recognition and Image Analysis: Advances of Mathematical Theory and Applications, Interperiodica Publishing, V. 9, No. 4, August 1999.
Terziyan V., Puuronen S., Kaikova H., Handling Uncertainty by Decontextualizing Estimated Intervals, In: Proceedings of MISC'99 Workshop on Applications of Interval Analysis to Systems and Control with special emphasis on recent advances in Modal Interval Analysis, 24-26 February 1999, Universitat de Girona, Girona, Spain, pp. 111-121.
Flexible Arithmetic for Huge Numbers
with Recursive Series of Operations
Infinite Series of Arithmetical Operations
1. Addition: a b (we use as the basic operation);
2. Multiplication: b
aaaba ...* ;
3. Raising to a power: b
b aaaa *...** .
. ;1 ;))1((11
babaaaababannnn
times
111
...b
nnnn
aaaba
4. General case
• A recursive expansion of the set of ordinary arithmetical operations was investigated;
• The recursive arithmetical operation was defined, where n is the level of recursion starting with ordinary + (n=1);
• Basic properties of recursive operations were investigated, an algorithm for calculation of these operations was considered;
• The recursive counters’ were proposed for representation of huge integers, which are results of recursive operations, in a restricted memory.
ban
Some results
Published in
Terziyan V., Tsymbal A., Puuronen S., Flexible Arithmetic For Huge Numbers with Recursive Series of Operations, In: 13-th AAECC Symposium on Applied Algebra, Algebraic Algorithms, and Error-Correcting Codes, 15-19 November 1999, Hawaii, USA.
A Similarity Evaluation Technique for Cooperative Problem Solving with a Group of Agents
Goal
The goal of this research is to develop simple similarity evaluation technique to be used for cooperative problem solving based on opinions of several agents
Problem solving here is finding of an appropriate solution for the problem among available ones based on opinions of several agents
Basic Concepts:Virtual Training Environment (VTE)
VTE of a group of agents is a quadruple:
<D,C,S,P>• D is the set of problems D1, D2,..., Dn in the VTE;
• C is the set of solutions C1, C2,..., Cm , that are used to solve the problems;
• S is the set of agents S1, S2,..., Sr , who selects solutions to solve the problems;
• P is the set of semantic predicates that define relationships between D, C, S
External Similarity Values
DC
S
DiCj
Sk
SDk,i
DCi,j
SCk,j
External Similarity Values (ESV): binary relations DC, SC, and SD between the elements of (sub)sets of D and C; S and C; and S and D.
ESV are based on total support among all the agents for voting for the appropriate connection (or refusal to vote)
Internal Similarity Values
D C
S
Di’
SSk’,k’’
DDi’,i’’ CCj’,j’’
Di’’
Cj’
Cj’’
Sk’
Sk’’
Internal Similarity Values (ISV): binary relations between two subsets of D, two subsets of C and two subsets of S.
ISV are based on total support among all the agents for voting for the appropriate connection (or refusal to vote)
Why we Need Similarity Values (or Distance Measure) ? Distance between problems is used by agents to
recognize nearest solved problems for any new problem
distance between solutions is necessary to compare and evaluate solutions made by different agents
distance between agents is useful to evaluate weights of all agents to be able to integrate them by weighted voting.
Deriving External Relation DC:How well solution fits the problem
DC CD P D C S D D C Ci j j i i j k i jk
r
, , ( , , ), ,
DC
S
DiCj
Sk2
DCi,j=3
Sk1
Sk3
Agents
Problems Solutions
Deriving External Relation SC: Measures Agents Competence in the Area of Solutions
The value of the relation (Sk,Cj) in a way represents the total support that the agent Sk obtains selecting (refusing to select) the solution Cj to solve all the problems.
SC CS DC P D C S S S C Ck j j k i j i j ki
n
k j, , , ( , , ), ,
Deriving External Relation SD: Measures Agents Competence in the Problem’s Area
The value of the relation (Sk,Di) represents the total support that the agent Sk receives selecting (or refusing to select) all the solutions to solve the problem Di.
SD DS DC P D C S S S D Dk i i k i j i j kj
m
k i, , , ( , , ), ,
Agent’s Evaluation:Competence Quality in Problem Area
Q Sn
SDDk k i
i
n( ) , 1
- measure of the abilities of an agent in the area of problems from the support point of view
Agent’s Evaluation:Competence Quality in Solutions’ Area
- measure of the abilities of an agent in the area of solutions from the support point of view
Q Sm
SCCk k j
j
m( ) , 1
Quality Balance Theorem
Q S Q SDk
Ck( ) ( )
The evaluation of an agent competence (ranking, weighting, quality evaluation) does not depend on the competence area “virtual world of problems” or “conceptual world of solutions” because both competence values are always equal.
Internal Similarity for Agents:Problems-based Similarity
D C
SS’S’’D
S’’
S’DS’’
S’D
S S S S S S S D DSD' '' ' '' ' '',
Problems
Agents
Internal Similarity for Agents:Solutions-Based Similarity
D C
SS’S’’C
S’’
S’
CS’’
S’C
S S S S S S S C CSC' '' ' '' ' '',
Solutions
Agents
Internal Similarity for Agents:Solutions-Problems-Based Similarity
D C
SS’S’’CD
S’’
S’DS’’S’C
CD
S S S S S S S C CD DSCD' '' ' '' ' '',
Agents
SolutionsProblems
Conclusion
Discussion was given to methods of deriving the total support of each binary similarity relation. This can be used, for example, to derive the most supported solution and to evaluate the agents according to their competence
We also discussed relations between elements taken from the same set: problems, solutions, or agents. This can be used, for example, to divide agents into groups of similar competence relatively to the problems-solutions environment
Published in
Puuronen S., Terziyan V., A Similarity Evaluation Technique for Cooperative Problem Solving with a Group of Agents, In: M. Klush, O. M. Shegory, G. Weiss (Eds.), Cooperative Information Agents III, Lecture Notes in Artificial Intelligence, Springer-Verlag, V. 1652, 1999, pp. 163-174.
On-Line Incremental Instance-Based Learning
• How to derive the most supported knowledge (on-line prediction or classification) from the multiple experts (ensemble of classifiers);
• how to make quality evaluation of the most supported opinion (of the ensemble prediction);
• how to make, evaluate, use and refine ranks (weights) of all the experts (predictors) to improve the results of the on-line learning algorithm.
The Problems Addressed
The following problems has been investigated both on-line learning for human experts and for artificial predictors:
Results Published in
Kaikova H., Terziyan V., Temporal Knowledge Acquisition From Multiple Experts, In: Shoval P. & Silberschatz A. (Eds.), Proceedings of NGITS’97 - The Third International Workshop on Next Generation Information Technologies and Systems, Neve Ilan, Israel, June - July, 1997, pp. 44 - 55.
Puuronen S., Terziyan V., Omelayenko B., Experimental Investigation of Two Rank Refinement Strategies for Voting with Multiple Experts, Artificial Intelligence, Donetsk Institute of Artificial Intelligence, V. 2, 1988, pp. 25-41.
Omelayenko B., Terziyan. V., Puuronen S., Managing Training Examples for Fast Learning of Classifiers Ranks, In: CSIT’99 - International Workshop on Computer Science and Information Technologies, January 1999, Moscow, Russia, pp. 139-148.
Puuronen S., Terziyan V., Omelayenko B., Multiple Experts Voting: Two Rank Refinement Strategies, In: Integrating Technology & Human Decisions: Global Bridges into the 21st Century, Proceedings of the D.S.I.’99 5-th International Conference, 4-7 July 1999, Athens, Greece, V. 1, pp. 634-636.
We will be Happy to Cooperate with You !
Ф илиал-каф едра
Advanced Diagnostics Algorithms in Online Field
Device Monitoring Vagan Terziyan (editor)
http://www.cs.jyu.fi/ai/Metso_Diagnostics.ppt
“Industrial Ontologies” Group: http://www.cs.jyu.fi/ai/OntoGroup/index.html
“Industrial Ontologies” Group, Agora Center, University of Jyväskylä, 2003
Contents
Introduction: OntoServ.NetOntoServ.Net – Global “Health-Care” Environment for Industrial Devices;
Bayesian MetanetworksBayesian Metanetworks for Context-Sensitive Industrial Diagnostics;
Temporal Industrial DiagnosticsTemporal Industrial Diagnostics with Uncertainty;
Dynamic IntegrationDynamic Integration of Classification Algorithms for Industrial Diagnostics;
Industrial Diagnostics with Real-Time Neuro-Real-Time Neuro-Fuzzy SystemsFuzzy Systems;
Conclusion.
Vagan Terziyan
Oleksiy Khriyenko
Oleksandr Kononenko
Andriy Zharko
Web Services for Smart DevicesWeb Services for Smart Devices
Smart industrial devices can be also Web Service “users”. Their embedded agents are able to monitor the state of appropriate device, to communicate and exchange data with another agents. There is a good reason to launch special Web Services for such smart industrial devices to provide necessary online condition monitoring, diagnostics, maintenance support, etc.
OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, Tekes Project Proposal, March 2003,
Global Network of Maintenance ServicesGlobal Network of Maintenance Services
OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, Tekes Project Proposal, March 2003,
Embedded Maintenance PlatformsEmbedded Maintenance Platforms
Service Agents
Host Agent
Embedded Platform
Based on the online diagnostics, a service agent, selected for the
specific emergency situation, moves to the embedded platform to help the host agent to
manage it and to carry out the predictive
maintenance activities
Maintenance Service
OntoServ.NetOntoServ.Net Challenges Challenges
New group of Web service users – smart industrial smart industrial devicesdevices.
InternalInternal (embedded) and externalexternal (Web-based) agent enabled service platformsservice platforms.
“Mobile Service ComponentMobile Service Component” concept supposes that any service component can move, be executed and learn at any platform from the Service Network, including service requestor side.
Semantic Peer-to-PeerSemantic Peer-to-Peer concept for service network management assumes ontology-based decentralized service network management.
Agents in Semantic WebAgents in Semantic Web
1. “I feel bad, pressure more than 200,
headache, … Who can advise what to do ? “
4. “Never had such experience. No
idea what to do”
3. “Wait a bit, I will give you some pills”
2. “ I think you should stop drink beer for a while “
Agents in Semantic Web supposed to understand each other because they will share common standard, platform, ontology and language
The Challenge: The Challenge: GGlobal lobal UUnderstanding nderstanding eeNNvironmentvironment ( (GUNGUN))
How to make entities from our physical world to understand
each other when necessary ?..
… Its elementary ! But not easy !! Just to make agents from them !!!
GUN ConceptGUN Concept
Entities will interoperate through OntoShells, which are “supplements” of these
entities up to Semantic Web
enabled agents
1. “I feel bad, temperature 40, pain in stomach, … Who can advise what to do ? “
2. “I have some pills for you”
Semantic Web: Before GUNSemantic Web: Before GUN
Semantic Web Resources
Semantic Web Applications
Semantic Web applications “understand”, (re)use, share, integrate, etc. Semantic Web
resources
GUN Concept:GUN Concept: All GUN resources “understand” each otherAll GUN resources “understand” each other
Real World objects
OntoAdapters
Real World Object ++ OntoAdapter +
+ OntoShell == GUN ResourceGUN Resource
GUNGUN
OntoShells
Real World objects of new generation (OntoAdapter inside)
Read Our ReportsRead Our Reports
Semantic Web: The Future Starts TodaySemantic Web: The Future Starts Today– (collection of research papers and presentations of Industrial Ontologies
Group for the Period November 2002-April 2003)
Semantic Web and Peer-to-Peer: Semantic Web and Peer-to-Peer: Integration and Interoperability in IndustryIntegration and Interoperability in Industry
Semantic Web Enabled Web Services: Semantic Web Enabled Web Services: State-of-Art and ChallengesState-of-Art and Challenges
Distributed Mobile Web Services Based on Semantic Web: Distributed Mobile Web Services Based on Semantic Web: Distributed Industrial Product Maintenance SystemDistributed Industrial Product Maintenance System
Available online in: http://www.cs.jyu.fi/ai/OntoGroup/index.html
Industrial Ontologies GroupIndustrial Ontologies Group
V. Terziyan
A. Zharko
O. Kononenko
O. Khriyenko
Vagan Terziyan
Oleksandra Vitko
Example of Simple Bayesian Network
X
Y
P(X)
P(Y)-?
P(Y|X)
n
iiin XParentsXPXXXP
121 ))(|(),...,,(
)|()(),( ijiij xXyYPxXPxXyYP
i
ijij xXyYPxXPyYP )|()()(
)(
)|()()|(
j
ijiji yYP
xXyYPxXPyYxXP
Conditional (in)dependence rule
Joint probability rule
Marginalization rule
Bayesian rule
Contextual and Predictive Attributes
Machine
Environment
Sensors
XX x1 x2 x3 x4 x5 x6 x7
predictive attributes contextual attributes
air pressure
dust
humidity
temperature
emission
Contextual Effect on Conditional Probability
XX x1 x2 x3 x4 x5 x6 x7
predictive attributes contextual attributes
xk xr
Assume conditional dependence between predictive attributes
(causal relation between physical quantities)…
xt
… some contextual attribute may effect
directly the conditional dependence between
predictive attributes but not the attributes itself
Contextual Effect on Conditional Probability
X
Y
P(X)
P(Y)-? P(P(Y|X)|Z)
Z
P(Z) P(Y|X)
pk(Y|X)
P(P(Y|X))
•X ={x1, x2, …, xn} – predictive attribute with
n values;•Z ={z1, z2, …, zq} – contextual attribute with q
values;•P(Y|X) = {p1(Y|X), p2(Y|X), …, p r(Y|X)} –
conditional dependence attribute (random variable) between X and Y with r possible values;•P(P(Y|X)|Z) – conditional dependence between attribute Z and attribute P(Y|X);
})]|)|()|(()([
)()|({)(
1
1 1
q
mmkm
r
k
n
iiijkj
zZXYpXYPPzZP
xXPxXyYpyYP
Contextual Effect on Unconditional Probability
XX x1 x2 x3 x4 x5 x6 x7
predictive attributes contextual attributes
xk
Assume some predictive attribute is a random
variable with appropriate probability distribution
for its values…
xt
… some contextual attribute may effect
directly the probability distribution of the predictive attribute
x1 x2 x3x4
XX
P(X)P(X)
Contextual Effect on Unconditional Probability
X
Y
P(Y)-? P(P(X)|Z)
Z
P(Z)
P(X)
pk(X)
P(P(X))
P(Y|X)
X ={x1, x2, …, xn} – predictive attribute with n
values;
· Z ={z1, z2, …, zq} – contextual attribute with q values
and P(Z) – probability distribution for values of Z;
• P(X) = {p1(X), p2(X), …, pr(X)} – probability
distribution attribute for X (random variable) with r possible values (different possible probability distributions for X) and P(P(X)) is probability distribution for values of attribute P(X);
· P(Y|X) is a conditional probability distribution of Y given X;
· P(P(X)|Z) is a conditional probability distribution
for attribute P(X) given Z
})]|)()(()([
)()|({)(
1
1 1
q
mmkm
r
k
n
iikijj
zZXpXPPzZP
xXpxXyYPyYP
Bayesian Metanetworks for Advanced Diagnostics
3-level Bayesian Metanetwork forManaging Feature Relevance
X
Y
A
BQ
RSX
Y
A
B
Q
RS
2 -lev e l B ay esian M etan e tw o rk fo rm o d e llin g re lev an t fea tu res’ se lec tio n
C o n te x tu a l le ve l
P re d ic tiv e le v e l
Two-level Bayesian Metanetwork formanaging conditional dependencies
X
Y
A
BQ
RS
X
Y
A
B
Q
RS
T w o -lev e l B ay esian M etan e tw o rk fo rm an ag in g co n d itio n a l d ep en d en c ies
C o n te x tu a l le ve l
P re d ic tiv e le v e l
Terziyan V., Vitko O., Probabilistic Metanetworks for Intelligent Data Analysis, Artificial Intelligence, Donetsk Institute of Artificial Intelligence, Vol. 3, 2002, pp. 188-197.
Terziyan V., Vitko O., Bayesian Metanetwork for Modelling User Preferences in Mobile Environment, In: German Conference on Artificial Intelligence (KI-2003), Hamburg, Germany, September 15-18, 2003.
Two-level Bayesian Metanetwork for managing conditional dependencies
Contextual level
Predictive level A
B
X
Y
P(B|A) P(Y|X)
Causal Relation between Conditional Probabilities
xk xr
xm xn
P1(Xn|Xm)
P(XP(Xnn| X| Xmm))
P(P(XP(P(Xnn| X| Xmm))))
P2(Xn|Xm) P3(Xn|Xm)
P1(Xr|Xk)
P(XP(Xrr| X| Xkk))
P(P(XP(P(Xrr| X| Xkk))))
P2(Xr|Xk)
P(P(XP(P(Xrr| X| Xkk)|P(X)|P(Xnn| X| Xmm))))
There might be causal relationship between two pairs of
conditional probabilities
Example of Bayesian MetanetworkThe nodes of the 2nd-level network correspond to the conditional probabilities of the 1st-level network P(B|A) and P(Y|X). The arc in the 2nd-level network corresponds to the conditional probability P(P(Y|X)|P(B|A))
X
Y
P(X)
P(Y)-?
P(P(Y|X)|P(B|A))
A
B
P(A)
pr(B|A)
P(P(B|A)) P(B|A) P(Y|X)
pk(Y|X)
P(P(Y|X))
))]}.|()|(())|()|((|)|()|(([
)()|({)(
ABpABPPXYpABPPXYpXYPP
xXPxXyYpyYP
rr
rk
i kiijkj
Other Cases of Bayesian Metanetwork (1)
P(A) P(X)
X
A
Contextual level
Predictive level
a)
P(P(X)|P(A))
A
pr(A)
P(P(A))
P(A) X
pk(X)
P(P(X))
P(X)
b)
Unconditional probability distributions associated with nodes of the predictive level network depend on probability distributions associated with nodes of the contextual level network
Other Cases of Bayesian Metanetwork (2)
The metanetwork on the contextual level models conditional dependence particularly between unconditional and conditional probabilities of the predictive level
P(A) P(Y|X)
X A
Contextual level
Predictive level
Y
c)
X
Y
P(X)
P(Y)-? P(P(Y|X)|P(A))
A
pr(A)
P(P(A))
P(A) P(Y|X)
pk(Y|X)
P(P(Y|X))
d)
Other Cases of Bayesian Metanetwork (3)
The combination of cases 1 and 2
P(A)
P(Y|X)
X A
Contextual level
Predictive level
Y
P(B)
B
e)
X
Y
P(X)
P(Y)-?
P(P(Y|X)|P(A))
A
pr(A)
P(P(A))
P(A)
P(Y|X)
pk(Y|X)
P(P(Y|X))
B
ps(B) P(P(B))
P(B)
P(P(A)|P(B))
f)
Contextual level
Predictive level
2-level RelevanceRelevance Bayesian Metanetwork (for modelling relevant features’ selection)
Simple Relevance Bayesian MetanetworkWe consider relevance as a probability of importance of the variable to the inference of target attribute in the given context. In such definition relevance inherits all properties of a probability.
X
Y
Probability
P(X)
P(Y)-? P(Y|X)
Relevance
Ψ(X)
X
Y
P(X)
P(Y|X)
Probability to have this model is:
P((X)=”yes”)= X
Y
P0(Y) Probability to have this model is:
P((X)=”no”)= 1-X
.)]1()([)|(1
)( X
XX XPnxXYPnx
YP
Example of 2-level Relevance Bayesian Metanetwork
In a relevance network the relevancies are considered as random variables between which the conditional dependencies can be learned.
X
Y
P(X)
P(Y)-?
P(Y|X) P(Ψ(X)|Ψ(A))
A
P(A) Ψ(A) Ψ(X)
)]}.1()()|()([)|({1
)( XAAXX A
PPXPnxXYPnx
YP
More Complicated Case of Managing Relevance (1)
X
Y
Probability
P(X)
P(Y)-?
P(Y|X,Z)
Relevance
Ψ(X)
Z
Probability
P(Z) Relevance
Ψ(Z)
X
Y
Probability
P(X)
P(Y|X,Z)
Z
Probability
P(Z)
Probability of this case is equal to:
P((X)=”yes”)×P((Z)=”yes”) = = X·Z
11
X
Y
Probability
P(X)
P(Y|X)
Probability of this case is equal to:
P((X)=”yes”)×P((Z)=”no”) = = X·(1-Z)
Y
P(Y|Z)
Z
Probability
P(Z)
Probability of this case is equal to:
P((X)=”no”)×P((Z)=”yes”) = = (1-X)·Z
Y
Probability
P0(Y)
Probability of this case is equal to:
P((X)=”no”)×P((Z)=”no”) = = (1-X)·(1-Z)
22 33 44
More Complicated Case of Managing Relevance (2)
X
Y
Probability
P(X)
P(Y)-?
P(Y|X,Z)
Relevance
Ψ(X)
Z
Probability
P(Z) Relevance
Ψ(Z)
,),|(1
)1()1(
)(),|(1
)1(
)(),|(1
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1
1
1
1
nx
i
nz
ikkiZX
nx
i
nz
ikkkiZX
nx
i
nz
ikikiZX
nx
i
nz
ikkikiZX
zZxXYPnznx
zZPzZxXYPnx
xXPzZxXYPnz
zZPxXPzZxXYPYP
General Case of Managing Relevance (1)
X1
Y
Probability
P(X1)
P(Y)-?
P(Y|X1,X2,…,XN)
Relevance
Ψ(X1)
XN
Probability
P(XN) Relevance
Ψ(XN) X2
Probability
P(X2) Relevance
Ψ(X2)
…
Predictive attributes:
X1 with values {x11,x12,…,x1nx1};
X2 with values {x21,x22,…,x2nx2};
…XN with values {xn1,xn2,…,xnnxn};
Target attribute:
Y with values {y1,y2,…,yny}.
Probabilities:
P(X1), P(X2),…, P(XN);P(Y|X1,X2,…,XN).
Relevancies:X1 = P((X1) = “yes”);
X2 = P((X2) = “yes”);
…XN = P((XN) = “yes”);
Goal: to estimate P(Y).
General Case of Managing Relevance (2)
X1
Y
Probability
P(X1)
P(Y)-?
P(Y|X1,X2,…,XN)
Relevance
Ψ(X1)
XN
Probability
P(XN) Relevance
Ψ(XN) X2
Probability
P(X2) Relevance
Ψ(X2)
…
1 2 )"")(()"")((
1
])1()(),...2,1|([...1
)(X X XN noXqq
XqyesXrr
XrN
s
XrPnxrXNXXYPnxs
YP
Example of Relevance MetanetworkX
Y
A
BQ
RS
a)
X
Y
A
B
Q
RS
b)c)
Relevance level
Predictive level
Combined Bayesian Metanetwork
In a combined Metanetwork two controlling
(contextual) levels will effect the basic level
Contextual level A
Predictive level
Contextual level B
Learning Bayesian Metanetworks from Data Learning Bayesian Metanetwork structure
(conditional, contextual and relevance (in)dependencies at each level);
Learning Bayesian Metanetwork parameters (conditional and unconditional probabilities and relevancies at each level).
Vitko O., Multilevel Probabilistic Networks for Modelling Complex Information Systems under Uncertainty, Ph.D. Thesis, Kharkov National University of Radioelectronics, June 2003. Supervisor: Terziyan V.
When Bayesian Metanetworks ?
1. Bayesian Metanetwork can be considered as very powerful tool in cases where structure (or strengths) of causal relationships between observed parameters of an object essentially depends on context (e.g. external environment parameters);
2. Also it can be considered as a useful model for such an object, which diagnosis depends on different set of observed parameters depending on the context.
Vagan Terziyan
Vladimir Ryabov
Temporal Diagnostics of Field Devices• The approach to temporal diagnostics uses the algebra of uncertain temporal relations*.
• Uncertain temporal relations are formalized using probabilistic representation.
• Relational networks are composed of uncertain relations between some events (set of symptoms)
• A number of relational networks can be combined into a temporal scenario describing some particular course of events (diagnosis).
• In future, a newly composed relational network can be compared with existing temporal scenarios, and the probabilities of belonging to each particular scenario are derived.
* Ryabov V., Puuronen S., Terziyan V., Representation and Reasoning with Uncertain Temporal Relations, In: A. Kumar and I. Russel (Eds.), Proceedings of the Twelfth International Florida AI Research Society Conference - FLAIRS-99, AAAI Press, California, 1999, pp. 449-453.
Conceptual Schema for Temporal Diagnostics
N
S1 S2 … Sn
Temporal scenarios
1,SND2,SND
nSND ,
Recognition of temporal scenarios
• We estimate the probability of belonging of the particular relational network to known temporal scenarios.
Generating temporal scenarios
• We compose a temporal scenario combining a number of relational networks consisting of the same set of symptoms and possibly different temporal relations between them.
N1
N2
N3
N4N5
S
Terziyan V., Ryabov V., Abstract Diagnostics Based on Uncertain Temporal Scenarios, International Conference on Computational Intelligence for Modelling Control and Automation CIMCA’2003, Vienna, Austria, 12-14 February 2003, 6 pp.
Industrial Temporal Diagnostics (conceptual schema)
Industrial object
Temporal data
Relational network
DB ofscenarios
Estimation Recognition Diagnosis
Learning
Ryabov V., Terziyan V., Industrial Diagnostics Using Algebra of Uncertain Temporal Relations, IASTED International Conference on Artificial Intelligence and Applications, Innsbruck, Austria, 10-13 February 2003, 6 pp.
Event 2
< a1; a2; a3 > - imperfect temporal relation
between temporal points (Event 1 and Event 2):
P(event 1, before, event 2) = a1;
P(event 1, same time, event 2) = a2;
P(event 1, after, event 2) = a3.
Event 1
< a1; a2; a3 >
Imperfect Relation Between Temporal Point Events: Definition
Ryabov V., Handling Imperfect Temporal Relations, Ph.D. Thesis, University of Jyvaskyla, December 2002. Supervisors: Puuronen S., Terziyan V.
Example of Imperfect Relation
Event 2
< 0.5; 0.2; 0.3 > - imperfect temporal relation between temporal points:
P(event 1, before, event 2) = 0.5;
P(event 1, same time, event 2) = 0.2;
P(event 1, after, event 2) = 0.3.
Event 1
< 0.5; 0.2; 0.3 >
1
<= >
R(Event 1,Event 2)
Operations for Reasoning with Temporal Relations
rb,a = bar,~
ra,b
a b
ra,b rb,c
ra,c = ra,b rb,c
a
b
c
r r ra b a b a b, , , 1 2
r 1 a , br 2 a , b
a b
Inversion
Sum
Composition
Temporal Interval Relations
The basic interval relations are the thirteen Allen’s relations:
A before (b) B B after (bi) A
A meets (m) B B met-by (mi) A
A overlaps (o) B B overlapped-by (oi) A
A starts (s) B B started-by (si) A
A during (d) B B contains (di) A
A finishes (f) B B finished-by (fi) A
A equals (eq) B B equals A
A B
AB
AB
BA
AB
AB
BA
Imperfect Relation Between Temporal Intervals: Definition
interval 2
< a1; a2;… ; a13 > - imperfect temporal relation between
temporal intervals (interval 1 and interval 2):
P(interval 1, before, interval 2) = a1;
P(interval , meets, interval 2) = a2;
P(interval 1, overlaps, interval 2) = a3;
…
P(interval 1, equals, interval 2) = a13;
interval 1
< a1; a2 ;… ; a13 >
Industrial Temporal Diagnostics (composing a network of relations)
Sensor 3Sensor 2
Relational network representing the particular caseIndustrial object
Sensor 1
Estimation of temporal relations between
symptoms
Industrial Temporal Diagnostics (generating temporal scenarios)
N1
Scenario S
N3N2
Object A Object B Object C
Generating the temporal scenario
for “Failure X”DB of
scenarios
1. for i=1 to n do
2. for j=i+1 to n do
3. if (R1) or…or (Rk) then
4. begin
5. for g=1 to n do
6. if not (Rg) then Reasoning(, Rg)
7. // if “Reasoning” = False then (Rg)=TUR
8. ( R) = Å ( Rt), where t=1,..k
9. end
10. else go to line 2
Recognition of Temporal Scenario
m
ii
m
iii
w
dwD
1
1SN,
)Bal()Bal(,, , DC,BA,DCBA
RRd RR
12
0,
1
12
1
i
iei BABal(RA,B) =
Industrial object
Temporal data
Relational network
DB ofscenarios
Estimation Recognition Diagnosis
Learning
bm
ofi
disi eq
sd
foi
mi
bi
wbi =1
weq
=0.5
wb =0 wf =0.75
Balance point for RA,B
Balance point for RC,D
Probability value
When Temporal Diagnostics ?
1. Temporal diagnostics considers not only a static set of symptoms, but also the time during which they were monitored. This often allows having a broader view on the situation, and sometimes only considering temporal relations between different symptoms can give us a hint to precise diagnostics;
2. This approach might be useful for example in cases when appropriate causal relationships between events (symptoms) are not yet known and the only available for study are temporal relationships;
3. Combination of Bayesian (based on probabilistic causal knowledge) and Temporal Diagnostics would be quite powerful diagnostic tool.
Terziyan V., Dynamic Integration of Virtual Predictors, In: L.I. Kuncheva, F. Steimann, C. Haefke, M. Aladjem, V. Novak (Eds), Proceedings of the International ICSC Congress on Computational Intelligence: Methods and Applications - CIMA'2001, Bangor, Wales, UK, June 19 - 22, 2001, ICSC Academic Press, Canada/The Netherlands, pp. 463-469.
VaganTerziyan
The Problem
During the past several years, in a variety of application domains, researchers in machine learning, computational learning theory, pattern recognition and statistics have tried to combine
efforts to learn how to create and combine an ensemble of classifiers.
The primary goal of combining several classifiers is to obtain a more accurate prediction than can be obtained from any single classifier alone.
Approaches to Integrate Multiple Classifiers
Integrating Multiple Classifiers
Selection Combination
Global (Static)
Local (Dynamic)
Local (“Virtual” Classifier)
Global (Voting-Type)
Decontextualization
Inductive learning with integration of predictors
rrmrr yxxx ,...,, 21
Sample Instances
tmtt xxx ,...,, 21
yt
Learning Environment
P1 P2 ... Pn
Predictors/Classifiers
Virtual Classifier
TeamtionClassificasInstructorTeam
Members Team Elective
Members TeamConstant
, CLDE,FS,TITP,TM,TC,
TC - Team Collector
TM - Training Manager
TP - Team Predictor
TI - Team Integrator
FS - Feature Selector
DE - Distance Evaluator
CL - Classification Processor
Virtual Classifier is a group of seven cooperative agents:
Classification Team: Feature Selector
TeamtionClassificasInstructorTeam
Members Team Elective
Members TeamConstant
, CL DE, ,TI TP, TM, TC, FS
FS - Feature Selector
Feature Selector:
finds the minimally sized feature subset that is sufficient for correct classification of the instance
Fea
ture
Sel
ecto
r
Sample InstancesSample Instances
rr yΧrr
' ΧΧΧ ,'rr y
Classification Team: Distance Evaluator
TeamtionClassificasInstructorTeam
Members Team Elective
Members TeamConstant
, CL , FS,TI TP, TM, TC, DE
DE - Distance Evaluator
Distance between Two Instances with Heterogeneous Attributes (example)
YyXxi
iii
ii
yxdYXD,,
2),(),(
i
ii
ii
ii
range
yx
yxi
yxd
:else
otherwise ,1
if ,0 - nominal is attributeth if
),(
where:
d (“red”, “yellow”) = 1 d (15°, 25°) = 10°/((+50°)-(-50°)) = 0.1
Distance Evaluator:
measures distance between instances based on their numerical or nominal attribute values
Distance Evaluator
imii xxx ,...,, 21 jmjj xxx ,...,, 21
ijd
Classification Team: Classification Processor
TeamtionClassificasInstructorTeam
Members Team Elective
Members TeamConstant
, CL DE, FS,TI TP, TM, TC,
CL - Classification Processor
Classification Processor:
predicts class for a new instance based on its selected features and its location relatively to sample instances
Classification Processor
imii xxx ,...,, 21
iy
Sample Instances
Feature Selector
Distance Evaluator
Team Instructors:Team Collector
TeamtionClassificasInstructorTeam
Members Team Elective
Members TeamConstant
, CL DE, FS,TI TP, TM, TC,
TC - Team Collector completes Classification Teams for training
Team Collector
completes classification teams for future training
Team Collector FSi DEj CLk
Feature Selection methods
Distance Evaluation functions
Classification rules
Team Instructors:Training Manager
TeamtionClassificasInstructorTeam
Members Team Elective
Members TeamConstant
, CL DE, FS,TI TP, , TC, TM
TM - Training Manager trains allcompleted teams on sample instances
Training Manager
trains all completed teams on sample instances
Training Manager
FSi1 DEj1CLk1
FSi2 DEj2CLk2
FSin DEjnCLkn
rrmrr yxxx ,...,, 21
Sample Instances
rnrrrmrr wwwxxx ,...,,,...,, 2121
Sample Metadata
Classification Teams
Team Instructors:Team Predictor
TeamtionClassificasInstructorTeam
Members Team Elective
Members TeamConstant
, CL DE, FS,TI , TM, TC, TP
TP - Team Predictor predicts weights forevery classification team in certain location
Team Predictor
predicts weights for every classification team in certain location
Team Predictor:
e.g. WNN algorithm
rnrrrmrr wwwxxx ,...,,,...,, 2121
Sample Metadata
imii xxx ,...,, 21 inii www ,...,, 21
Predicted weightsof classification teamsLocation
Team Prediction:Locality assumption
Each team has certain subdomains in the space of instance attributes, where it is more reliable than the others;
This assumption is supported by the experiences, that classifiers usually work well not only in certain points of the domain space, but in certain subareas of the domain space [Quinlan, 1993];
If a team does not work well with the instances near a new instance, then it is quite probable that it will not work well with this new instance also.
Team Instructors:Team Integrator
TeamtionClassificasInstructorTeam
Members Team Elective
Members TeamConstant
, CL DE, FS, , TP TM, TC, TI
TI - Team Integrator produces classificationresult for a new instance by integratingappropriate outcomes of learned teams
Team integrator
produces classification result for a new instance by integrating appropriate outcomes of learned teams
Tea
m In
teg
rato
r
FSi1 DEj1CLk1
FSi2 DEj2CLk2
FSin DEjnCLkn
tmtt xxx ,...,, 21
New instance
tntt www ,...,, 21
yt1
yt2
yt1
yt
Weights of classification teamsin the location of a new instance
Classification teams
Static Selection of a Classifier
Static selection means that we try all teams on a sample set and for further classification select one, which achieved the best classification accuracy among others for the whole sample set. Thus we select a team only once and then use it to classify all new domain instances.
Dynamic Selection of a Classifier
Dynamic selection means that the team is being selected for every new instance separately depending on where this instance is located. If it has been predicted that certain team can better classify this new instance than other teams, then this team is used to classify this new instance. In such case we say that the new instance belongs to the “competence area” of that classification team.
Conclusion
Knowledge discovery with an ensemble of classifiers is known to be more accurate than with any classifier alone [e.g. Dietterich, 1997].
If a classifier somehow consists of certain feature selection algorithm, distance evaluation function and classification rule, then why not to consider these parts also as ensembles making a classifier itself more flexible?
We expect that classification teams completed from different feature selection, distance evaluation, and classification methods will be more accurate than any ensemble of known classifiers alone, and we focus our research and implementation on this assumption.
Yevgeniy Bodyanskiy
Volodymyr Kushnaryov
Online Stochastic Faults’ PredictionControl Systems Research Laboratory, AI Department, Kharkov National University of Radioelectronics. Head: Prof. E. Bodyanskiy. Carries out research on development of mathematical and algorithmic support of systems for control, diagnostics, forecasting and emulation:
1. Neural network architectures and real-time algorithms for observation and sensor data processing (smoothing, filtering, prediction) under substantial uncertainty conditions;
2. Neural networks in polyharmonic sequence analysis with unknown non-stationary parameters;
3. Analysis of chaotic time series; adaptive algorithms and neural network architectures for early fault detection and diagnostics of stochastic processes;
4. Adaptive multivariable predictive control algorithms for stochastic systems under various types of constraints;
5. Adaptive neuro-fuzzy control of non-stationary nonlinear systems;
6. Adaptive forecasting of non-stationary nonlinear time series by means of neuro-fuzzy networks;
7. Fast real-time adaptive learning procedures for various types of neural and neuro-fuzzy networks.
Bodyanskiy Y., Vorobyov S, Recurrent Neural Network Detecting Changes in the Properties of Non-Linear Stochastic Sequences, Automation and Remote Control, V. 1, No. 7, 2000, pp. 1113-1124.
Bodyanskiy Y., Vorobyov S., Cichocki A., Adaptive Noise Cancellation for Multi-Sensory Signals, Fluctuation and Noise Letters, V. 1, No. 1, 2001, pp. 12-23.
Bodyanskiy Y., Kolodyazhniy V., Stephan A. An Adaptive Learning Algorithm for a Neuro-Fuzzy Network, In: B. Reusch (ed.), Computational Intelligence. Theory and Applications, Berlin-Heidelberg-New York: Springer, 2001, pp. 68-75.
Existing Tools
Most existing (neuro-) fuzzy systems used for fault diagnosis or classification are based on offline learning with the use of genetic algorithms or modifications of the error back propagation. When the number of features and possible fault situations is large, tuning of the classifying system becomes very time consuming. Moreover, such systems perform very poorly in high dimensions of the input space, so special modifications of the known architectures are required.
Neuro-Fuzzy Fault Diagnostics
Successful application of the neuro-fuzzy synergism to fault diagnosis of complex systems demands development of an online diagnosing system that quickly learns from examples even with a large amount of data, and maintains high processing speed and high classification accuracy when the number of features is large as well.
Challenge: Growing (Learning) Probabilistic Neuro-Fuzzy Network (1)
input layer,n inputs
1-st hidden layer,N neurons
2-nd hidden layer,(m+1) elements
output layer,m divisors
Bodyanskiy Ye., Gorshkov Ye., Kolodyazhniy V., Wernstedt J., Probabilistic Neuro-Fuzzy Network with Non-Conventional Activation Functions, In: Knowledge-Based Intelligent Information & Engineering Systems, Proceedings of Seventh International Conference KES’2003, 3–5 September, Oxford, United Kingdom, LNAI, Springer-Verlag, 2003.
Bodyanskiy Ye., Gorshkov Ye., Kolodyazhniy V. Resource-Allocating Probabilistic Neuro-Fuzzy Network, In: Proceedings of International Conference on Fuzzy Logic and Technology, 10–12 September, Zittau, Germany, 2003.
Challenge: Growing (Learning) Probabilistic Neuro-Fuzzy Network (2)
Implements fuzzy reasoning and classification (fuzzy classification fuzzy classification networknetwork);
Creates automatically neurons based on training set (growing growing networknetwork);
Learns free parameters of the network based on training set (learning networklearning network);
Guarantees high precision of classification based on fast learning (high- performance networkhigh- performance network);
Able to perform with huge volumes of data with limited computational resources (powerful and economical networkpowerful and economical network);
Able to work in real-time (real-time networkreal-time network).
Tested on real data in comparison with classical probabilistic neural network
Unique combination of features
Tests for Neuro-Fuzzy AlgorithmsIndustrial Ontologies Group (Kharkov’s Branch), Data Mining Research Group and Control Systems Research Laboratory of the Artificial Intelligence Department of Kharkov National University of Radioelectronics have essential theoretical and practical experience in implementing neuro-fuzzy approach and specifically Real-Time Probabilistic Neuro-Fuzzy Systems for Simulation, Modeling, Forecasting, Diagnostics, Clustering, Control .
We are interested in cooperation with Metso in that area and we are ready to present the performance of our algorithms on real data taken from any of Metso’s products to compare our algorithms with existing in Metso algorithms.
Inventions we can offer (1)
Method of intelligent preventive or predictive diagnostics and forecasting of technical condition of industrial equipment, machines, devices, systems, etc. in real time based on analysis of non-stationary stochastic signals (e.g. from sensors of temperature, pressure, current, shifting, frequency, energy consumption, and other parameters with threshold values).
The method is based on advanced data mining techniques, which utilize fuzzy-neuro technologies, and differs from existing tools by flexible self-organizing network structure and by optimization of computational resources while learning.
Inventions we can offer (2)
Method of intelligent real-time preventive or predictive diagnostics and forecasting of technical condition of industrial equipment, machines, devices, systems, etc. based on analysis of signals with non-stationary and non-multiplied periodical components (e.g. from sensors of vibration, noise, frequencies of rotation, current, voltage, etc.).
The method is based on optimization of computational resources while learning because of intelligent reducing of the number of signal components being analyzed.
Inventions we can offer (3)
Method and mechanism of optimal control of dosage and real-time infusion of anti-wear oil additives into industrial machines based on its real-time condition monitoring.
Summary of problems we can solve
Rather global system for condition monitoring and preventive maintenance based on OntoServ.Net (global, agent-based, ontology-based, Semantic Web services-based, semantic P2P search-based) technologies, modern and advanced data-mining methods and tools with knowledge creation, warehousing, and updating during not only device’s lifetime, but also utilizing (for various maintenance needs) knowledge obtained afterwards (various testing and investigations techniques other than information taken from “living” device’s sensors) from broken-down, worn out or aged components of the same type.
Recently Performed Case Studies (1) Ontology Development for Gas Compressing Equipment
Diagnostics Realized by Neural Networks
Available in: http://www.cs.jyu.fi/ai/OntoGroup/docs/July2003.pdf
VolodymyrKushnaryov
SemenSimkin
1212
NN and Ontology using for DiagnosticNN and Ontology using for Diagnostic
SENSOR
SIGNAL
Neural NetworkDiagnostic out
Training
Diagnosing
1515
The creating ontology classes The creating ontology classes instance programinstance program
The subclasses and their slots forming and The subclasses and their slots forming and instances filling by the information is instances filling by the information is carried out automatically with the program carried out automatically with the program on Java. The filling occurs from RDBMS on Java. The filling occurs from RDBMS Oracle, which contains in the Oracle, which contains in the actualizedactualizedbase using in ”base using in ”UkrTransGasUkrTransGas”.”.
OracleJava
Program Ontology
Recently Performed Case Studies (2) The use of Ontologies for Faults and State Description of Gas-
Transfer Units
Available in: http://www.cs.jyu.fi/ai/OntoGroup/docs/July2003.pdf
Agent
SCADA
Agent
SCADA
SCADA SCADA
Diagnosist Diagnosist
GTUGTU GTUGTU
Ontologyfor agent communication
VolodymyrKushnaryov
KonstantinTatarnikov
GTU-
MAINTENANCE
GTU
Control-type
Subsystem
GTU-State
Support-History
Period
Signal-Types
Repair-Reason
PARAMETER
ACTIONS
Shutdown
Launch
REPAIRMid-life Repair
Major Repair
Current Repair Planned Repair
GTU-Node
Compressorstation
SITUATIONS
Oil-temperaturedeviation
Axle-shear
Vibration
Rise-of-temperature
AnalogSignal
ComputeVariable
Trend
Conclusion
Industrial Ontologies Research GroupIndustrial Ontologies Research Group (University of Jyvaskyla), which is piloting the OntoServ.NetOntoServ.Net concept of the Global Semantic Web - Based System for Industrial Maintenance, has also powerful branches in Kharkovbranches in Kharkov (e.g. IOG-Kharkov’s Branch, Control Systems Research Laboratory, Data Mining Research Group, etc.) with experts and experiencesexperts and experiences in various and challenging data mining and knowledge discovery, online diagnostics, forecasting and control, models learning and integration, etc. methods, which can be and reasonable to be successfully utilized within going-on cooperation between MetsoMetso and Industrial Ontologies Group.
Find about our recent activities in:
http://www.cs.jyu.fi/ai/OntoGroup/projects.htm
http://www.cs.jyu.fi/ai/vagan/papers.html