WO1993021587A2 - Apprentissage machine a base de donnees relationnelles - Google Patents

Apprentissage machine a base de donnees relationnelles Download PDF

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Publication number
WO1993021587A2
WO1993021587A2 PCT/US1993/003558 US9303558W WO9321587A2 WO 1993021587 A2 WO1993021587 A2 WO 1993021587A2 US 9303558 W US9303558 W US 9303558W WO 9321587 A2 WO9321587 A2 WO 9321587A2
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Prior art keywords
database
record
response
predictive
actual value
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PCT/US1993/003558
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English (en)
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WO1993021587A3 (fr
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Bradley Paul Allen
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Inference Corporation
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Publication of WO1993021587A2 publication Critical patent/WO1993021587A2/fr
Publication of WO1993021587A3 publication Critical patent/WO1993021587A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • This invention relates to machine learning. More specifically, this invention relates to a machine learning system which uses a relational database.
  • rule-based systems of this type present a difficult programming task. Unlike more prosaic programming tasks, constructing a rule base is sometimes counterintuitive, and may be beyond the ability of many application programmers. And once a rule-based system has been constructed based on the knowledge of a human expert, it may be difficult to accommodate changes in the field of operation in which the processor must operate. Such changes might comprise advances in knowledge about the application field, additional tasks which are intended for the processor, or changes in or discoveries about the scope of the application field.
  • One proposed method of the prior art is to build automated reasoning systems which operate by reference to a set of exemplar cases (a "case base"), to which the facts of a particular situation (the "problem") may be matched.
  • the processor may then perform the same action for the problem as in the exemplar case. While this proposal has been well-received, case-based systems of this type may still require a substantial amount of human effort to identify exemplar cases and present a processor with sufficient information that cases may be matched and acted upon. For example, it may be necessary to deduce or supply extensive information about a complex environment so as to determine a preferred set of exemplar cases.
  • An aspect of the invention disclosed in that application also includes a technique in which a system may be set to work with a limited case base, and may solicit human advice for treatment of new problems which are not already well- treated by the case base, thus learning how to do its job on a dynamic basis.
  • an automated reasoning system may dynamically create its own case base in response to problems which it encounters, thus learning how to do its job on a dynamic basis and without substantial human intervention, or at least with only occasional human intervention.
  • an automated reasoning system may also operate autonomously in a complex environment, possibly with external intervention such as positive or negative reinforcing stimuli.
  • the external stimuli might be in response to a result of the system's attempts to manipulate its environment, or might be provided by an external agent, such as a human operator.
  • an external agent such as a human operator.
  • the invention provides a machine learning system which operates in conjunction with a relational database.
  • the machine learning system may (1) examine a selected entry in the database, (2) query the database for a set of entries which are representative of the selected entry, and (3) predict a value for one or more fields of the selected entry in response to the set of representative entries.
  • the system may perform these steps repeatedly, and may evaluate each entry and record an indication of accuracy or utility (or other values) of that entry for predicting one or more fields.
  • the invention also provides an implementation of a case-based-like reasoning system with a relational database.
  • the entries of the database may correspond generally to cases in a case-based reasoning system
  • the fields may correspond generally to features in a case-based reasoning system
  • searching the database may correspond generally to matching cases in a case base
  • predicting one or more fields may correspond generally to selecting a case to use in a case- based reasoning system.
  • Evaluating each entry may correspond generally to evaluating accuracy or utility (or other values) of cases for prescribing a correct action to take.
  • the invention also provides an implementation of an autonomous learning system with a relational database.
  • the machine learning system may implement an autonomous learning software agent,like that disclosed in copending application Serial No. , Lyon & Lyon Docket No. 193/304.
  • new entries in the database may be generated, deleted or modified by means of techniques which correspond generally to those by which new cases are generated, deleted or modified, as shown in that copending application, or as shown in parent copending application
  • the invention also provides a system for error- checking and correlating entries and fields in a relational database.
  • the predicted values for one or more fields of the selected entry may be compared with the actual values.
  • the system may note field values which
  • the system may "fill in" fields with the predicted values if actual values are missing or distrusted. Occasional or periodic error-checking and selective replacement of erroneous data may provide a self-repairing database.
  • the system may also note fields whose values which are easy to predict as possibly redundant, may note tuples of fields which are strongly correlated as possibly causally related, or may note fields whose values are difficult to predict as possibly requiring other data for good prediction.
  • Figure IA shows a data flow diagram of a method of machine learning with a relational database.
  • Figure IB shows a process flow diagram of a method of machine learning with a relational database.
  • Figure 2A shows a data flow diagram of a method of cluster recognition with a relational database.
  • Figure 2B shows a process flow diagram of a method of cluster recognition with a relational database.
  • Appendix A shows an example software environment and autonomous agent for distinguishing between classes of irises. Description of the Preferred Embodiment
  • Figure IA shows a data flow diagram of a method of machine learning with a relational database.
  • Figure IB shows a process flow diagram of a method of machine learning with a relational database.
  • a relational database 101 may comprise a set of records 102 and a set of fields 103, as is well known in the art.
  • Each field 103 in each record 102 may comprise a value 104, such, as a numeric value, a string value, or a value with another data type, as is well known in the art.
  • Relational databases are more fully described in "Principles of Database Systems", by Jeffery D. Ullman, published by Computer Science Press, hereby incorporated by reference as if fully set forth herein.
  • the database 101 may comprise at least one feature field 105 fl, f2, . . . fn, at least one predicted field 106 f*, and a set of evaluation fields 107 el, e2, . . . en.
  • the feature fields 105, predicted fields 106, and evaluation fields 107 are all found in a single database 101.
  • the database 101 may be organized in a variety of different ways consistent with the art of relational databases.
  • the evaluation fields 107 may form separate records 102 in a second database 101, correlated with the first database 101 by a set of record identifiers or by some similar technique, as is well known in the art. It would also be clear that many different ways of organization would be workable, and are within the scope and spirit of the invention.
  • a single record 102 in the database 101 may be designated as a selected record 109.
  • a database query or search designation 111 may be composed for records 102 which are "similar".
  • the search designation 111 may be specified in the SQL language, as is well known in the art.
  • other query languages or techniques for designating searches may also be used, that such other languages and techniques would be workable, and are within the scope and spirit of the invention.
  • Techniques for manipulating and querying databases using the SQL language are more fully described in "SQL Language Reference Manual (Version 5.1)", published by Oracle Corporation, hereby incorporated by reference as if fully set forth herein.
  • a set of similarity tables 112 may be maintained which indicate what records 102 are regarded as similar to the selected record 109.
  • the similarity tables 112 may indicate that a record 102 is similar to the selected record 109 if its value for the feature field 105 fl is within 0.1 numeric units, its value for the feature field 105 f2 is within 0.2 numeric units, its value for the feature field 105 f3 shares at least 3 common characters of text, and so on.
  • techniques for evaluating similarity such as like those disclosed in parent copending application Serial No. ,
  • the search designation 111 may be applied to the database 101, to produce a search set 114, comprising a set of records 102 which meet the search designation 111.
  • a predictor-selection step 115 one or more evaluation fields 107 of the records 102 in the search set 114 may be examined, and a predictive record 116 may be chosen for one or more predicted fields 106.
  • Techniques • such as like those used in the selector module of copending application Serial No. , Lyon &
  • a predicted value 117 for the predicted field 106 f* is the value for f* which is found in the predictive record 116.
  • the predicted value 117 from the predictive record 116 may be compared with the actual value 104 found in the selected record 109, and the evaluation fields 107 of the predictive record 116 may be updated accordingly.
  • the evaluation fields 107 may comprise fields for "times used", “times correct”, “accuracy”, “utility”, and other valuative measures such as like those disclosed in copending application Serial No. , Lyon &
  • Each record 102 of the database 101 may be designated as the selected record 109, and the process of designating a selected record 109, composing and applying a search designation 111, choosing a predictive record 116, comparing with the selected record 109 and updating the predictive record 116, may be performed repeatedly.
  • This causes values found in the evaluation fields 107 of the database 101 to reach an equilibrium state, in which they accurately represent, or at least closely approximate, the true accuracy and utility of the predictive value of each record 102.
  • a "predictive" database is a database 101 which is substantially in such a state
  • “predictive relaxation” is the technique of repeatedly updating the evaluation fields 107 which helps make a database 101 predictive.
  • predictive relaxation may be repeated so as to maintain the database 101 predictive.
  • predictive relaxation may be performed logically in parallel with other database operations such as adding, deleting or modifying records 102, so that the database 101 is maintained predictive even as it changes.
  • the database 101 may also be maintained predictive while the similarity tables 112 are altered.
  • a new record 119 is added to the predictive database 101, a set of predicted values 117 for one or more of its fields 103 may be determined, and the predicted values 117 compared with the actual values 104 from the new record 119.
  • the feature fields 105 and the predicted fields 106 may overlap, i.e., one or more feature fields 105 may also be predicted fields 106, so that any field 103 may be predicted. When more than one field is predicted, the evaluation fields 107 for predicting that field 103 may differ from the evaluation fields 107 for predicting another field 103.
  • the values 104 for those fields 103 may be filled in by predicting them. Thus, if field 103 f* is missing its value 104, the f* value 104 from the predictive record 116 may be inserted. Such values 104 might be inserted when the new record 119 is added, or at a later time. If one or more fields 103 in the new record 119 have values 104 which differ substantially from predicted, an alarm signal may be generated to indicate that such values 104 are erroneous, or at least should be checked. Such an alarm signal might be generated when the new record 119 is added, or might be generated occasionally as the database 101 is maintained predictive.
  • the distrusted values 104 might be replaced with the predictive values 104. Replacing such Values 104 might occur when the new record 119 is added, or might occur occasionally as the database 101 is maintained predictive. Alternatively, if the lack of an alarm signal indicates that values 104 in the new record 119 are probably correct, and the values 104 are otherwise distrusted, the distrusted values 104 might be marked trustworthy.
  • the database 101 may be maintained predictive in logical parallel with selectively replacing erroneous values 104, thus providing a database 101 which self-repairs any erroneous values 104 which are introduced in the course of adding, deleting, or modifying records 102.
  • Figure 2A shows a data flow diagram of a method of cluster recognition with a relational database.
  • Figure 2B shows a process flow diagram of a method of cluster recognition with a relational database.
  • the machine learning system may include a technique for cluster recognition.
  • the machine learning system may determine new records 102 for each feature field 105 which represent clusters 201 of values 104 for that feature field 105.
  • the new records 102 may be added to the database 101, or may be used to create a second database 101 which incorporates essentially the same information.
  • Knowledge about clusters 201 may also be used in the similarity tables 112, for example, to indicate that a value 104 for a feature field 105 of a record 102 is similar to a value 104 for the same feature field 105 of the selected record 109 if both values 104 are in the same cluster 201 for that feature field 105.
  • a feature field 105 is selected for cluster recognition.
  • an initial cluster-count of clusters 201 is set.
  • the initial cluster-count may be selected arbitrarily or randomly, by known statistical methods, or might be provided by an external agent, such as a human operator.
  • the initial cluster-count may always be set to four clusters 201.
  • cluster-count may be set to maximize "accuracy”, "utility”, and other valuative measures such as like those disclosed in that application, of the resulting set of clusters 201.
  • a cluster-centroid step 204 the range 205 of possible values 104 for the feature field 105 may be divided into subranges 206, one per cluster 201, and for each cluster 201, a cluster centroid 207 is selected.
  • each cluster centroid 207 may be selected arbitrarily or randomly within its subrange 206, but the cluster centroid 207 might be selected by a known averaging technique (such as the averaging technique used in the cluster-averaging step 209 herein, or provided by an external agent, such as a human operator.
  • a single cluster 201 may be selected and the database 101 may be interrogated for members of that cluster 201.
  • This step includes composing and applying a search designation 111 for the database 101, in similar manner as shown with respect to figures IA and IB.
  • a cluster-averaging step 209 an averaging technique is applied to the members of the cluster 201, in response to which a target centroid 210 may be determined.
  • the old cluster centroid 207 may be adjusted toward the target centroid 210, e.g., by an exponential decay technique.
  • a .predetermined fraction, such as 80%, of the difference between the old cluster centroid 207 and the target centroid 210 is added to the old cluster centroid 207 to determine a new cluster centroid 207.
  • the cluster-query step 208 and the cluster-averaging step 209 may be repeated until the target centroid 210 is determined to be within a predetermined threshold distance from the old cluster centroid 207.
  • a cluster-selection step 211 a new cluster 201 may be selected and the method may proceed with the cluster-centroid step 204.
  • the method may proceed with the cluster-setup step 203. The foregoing steps may be repeated for all clusters 201 for the selected feature field 105.
  • a second feature field 105 may be selected and the method may proceed with the cluster-setup step 203.
  • a set of clusters 201 for the second feature field 105 may be determined for each cluster 201 for the first feature field 105, forming a second level of a tree structure 213 of clusters 201.
  • the tree-structure step 212 may be repeated for succeeding feature fields 105 until it is performed for all nonsuperfluous feature fields 105.
  • a technique known as adaptive k- means clustering may be used to help determine the tree structure 213. Records embodying the tree structure 213 may be created as a second database 101 which incorporates essentially the same information. Where the database 101 is predictive, cluster recognition may be used as a technique which performs data compression and maintains the new database 101 predictive. Methods shown for the behavior module of copending application Serial No. , Lyon & Lyon
  • the machine learning system may use the methods shown in that copending application to determine which records are most "useful", i.e. which are good exemplar records for predicting values of f*, by analogy to those cases which would be good exemplar cases.
  • the machine learning system may tune the database 101 in several ways. It may add records 102 which are newly encountered, by analogy to adding cases which are new exemplar cases. It may remove those records 102 which are least "useful", by analogy to removing cases which are poor exemplar cases. It may generate new records 119 by a genetic technique, by analogy to generating new cases by a genetic technique. It may add such new records 119 to the database 101 and remove those new records 119 which fail to compete.
  • the machine learning system may also implement a case-based-like reasoning system with a relational database.
  • Cases in a case-based reasoning system may be represented by records 102 like those in the database 101, and the features of a case may be represented by the fields 103 of the record 102.
  • a case with two features with numerical values and one feature with a text value may be represented by a record 102 with two fields 103 with numerical values and one field 103 with a text value.
  • Cases in the case base may be represented by records 102 in the database 101, while cases which are encountered and which may be matched to the case base may be represented by records 102 which may be matched to the database 101.
  • a search designation 111 may be composed and applied so as to produce a search set 114 of records 102 which represent "similar" cases.
  • One of these records 102 may be chosen as the predictive record 116, which represents the case which is the "best match” .
  • the predicted fields 106 may represent the prescribed action for that case. For example, in a help-desk system the predicted fields 106 may indicate a voice response message and selection menu to be presented to the caller.
  • Evaluation of cases in a case-based system for accuracy and utility (or other values) may be represented by evaluating accuracy and utility (or other values) of the records 102 in the database 101.
  • the machine learning system may also implement an autonomous learning system, like that disclosed in copending application Serial No. , Lyon &
  • the autonomous learning system may comprise a case base in which cases are selected by a genetic technique, in which cases may be generated, deleted or modified. For example, new cases may be generated by altering features of cases already in the case base.
  • cases in a case-based reasoning system may be represented by records 102 like those in the database 101.
  • the records 102 in the database 101 may also be generated, deleted or modified by means of techniques like those disclosed in copending application
  • the machine learning system may generate new records 102 with fields 103 which correspond to those features and which represent those cases. Where those techniques delete cases, the machine learning system may remove the records 102 from the database 101 which correspond to those cases. Where those techniques modify cases (by modifying particular features of those cases) , the machine learning system may modify records 102 which correspond to those cases (by modifying fields 103 which correspond to those features) .
  • Appendix A shows an example software environment and autonomous agent for distinguishing between classes of irises.
  • the example software environment comprises an SQL table having a set of fields which relate to iris features, a set of SQL statements which exercise the machine learning system, and a machine learning system which makes the table predictive. Some exemplary data statements are also included.

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Abstract

Système d'apprentissage machine, fonctionnant conjointement avec une base de données relationnelles (101). Le système peut (1) examiner une entrée (109) choisie dans la base de données, (2) interroger la base de données à propos d'un ensemble d'entrées (114) représentatives de l'entrée choisie, et (3) prédire une valeur pour un ou plusieurs champs (106) de l'entrée choisie en réponse à l'ensemble d'entrées représentatives. Le système peut effectuer ces étapes de manière répétées et peut évaluer chaque entrée et enregistrer une indication de la précision ou de l'utilité (ou d'autres valeurs) (107) de cette entrée afin de prédire un ou plusieurs champs. Ce système peut également appliquer un système de raisonnement à base de cas, ou un système d'apprentissage autonome, avec une base de données relationnelles. Une vérification d'erreur occasionnelle ou périodique et le remplacement sélectif de données erronées permet d'obtenir une base de données à réparation autonome.
PCT/US1993/003558 1992-04-15 1993-04-14 Apprentissage machine a base de donnees relationnelles WO1993021587A2 (fr)

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WO2000036476A1 (fr) * 1998-12-17 2000-06-22 Siemens Aktiengesellschaft Systeme de raisonnement par cas pour prevision de valeurs fournies par des capteurs dans une installation technique, en particulier dans un four a ciment; procede et dispositif
US6336029B1 (en) 1996-12-02 2002-01-01 Chi Fai Ho Method and system for providing information in response to questions
US6498921B1 (en) 1999-09-01 2002-12-24 Chi Fai Ho Method and system to answer a natural-language question
EP1270827A1 (fr) * 2000-04-05 2003-01-02 Yamatake Corporation Systeme de prevision d'un volume d'eau a distribuer
US6571240B1 (en) 2000-02-02 2003-05-27 Chi Fai Ho Information processing for searching categorizing information in a document based on a categorization hierarchy and extracted phrases
US6862540B1 (en) * 2003-03-25 2005-03-01 Johnson Controls Technology Company System and method for filling gaps of missing data using source specified data
US7809663B1 (en) 2006-05-22 2010-10-05 Convergys Cmg Utah, Inc. System and method for supporting the utilization of machine language
US8379830B1 (en) 2006-05-22 2013-02-19 Convergys Customer Management Delaware Llc System and method for automated customer service with contingent live interaction
US20150142720A1 (en) * 2013-11-19 2015-05-21 Salesforce.Com, Inc. Predictive intelligence for service and support
US9690815B2 (en) 2013-03-13 2017-06-27 Salesforce.Com, Inc. Systems, methods, and apparatuses for implementing data upload, processing, and predictive query API exposure
US20180321683A1 (en) * 2017-05-02 2018-11-08 Cnh Industrial America Llc System and method for autonomous vehicle system planning
US11568281B2 (en) 2019-11-13 2023-01-31 International Business Machines Corporation Causal reasoning for explanation of model predictions

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Cited By (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6336029B1 (en) 1996-12-02 2002-01-01 Chi Fai Ho Method and system for providing information in response to questions
US6480698B2 (en) 1996-12-02 2002-11-12 Chi Fai Ho Learning method and system based on questioning
US6501937B1 (en) 1996-12-02 2002-12-31 Chi Fai Ho Learning method and system based on questioning
WO2000036476A1 (fr) * 1998-12-17 2000-06-22 Siemens Aktiengesellschaft Systeme de raisonnement par cas pour prevision de valeurs fournies par des capteurs dans une installation technique, en particulier dans un four a ciment; procede et dispositif
EP1014240A1 (fr) * 1998-12-17 2000-06-28 Siemens Aktiengesellschaft Système, procédé et appareil de déduction fondé sur des cas pour la prédiction d'un capteur dans une installation technique, en particulier dans un four à ciment
US6701195B2 (en) 1998-12-17 2004-03-02 Siemens Aktiengesellschaft Sensor prediction system utilizing case based reasoning
US6498921B1 (en) 1999-09-01 2002-12-24 Chi Fai Ho Method and system to answer a natural-language question
US6571240B1 (en) 2000-02-02 2003-05-27 Chi Fai Ho Information processing for searching categorizing information in a document based on a categorization hierarchy and extracted phrases
EP1270827A1 (fr) * 2000-04-05 2003-01-02 Yamatake Corporation Systeme de prevision d'un volume d'eau a distribuer
EP1270827A4 (fr) * 2000-04-05 2005-02-02 Yamatake Corp Systeme de prevision d'un volume d'eau a distribuer
US6862540B1 (en) * 2003-03-25 2005-03-01 Johnson Controls Technology Company System and method for filling gaps of missing data using source specified data
US7809663B1 (en) 2006-05-22 2010-10-05 Convergys Cmg Utah, Inc. System and method for supporting the utilization of machine language
US8379830B1 (en) 2006-05-22 2013-02-19 Convergys Customer Management Delaware Llc System and method for automated customer service with contingent live interaction
US9549065B1 (en) 2006-05-22 2017-01-17 Convergys Customer Management Delaware Llc System and method for automated customer service with contingent live interaction
US9690815B2 (en) 2013-03-13 2017-06-27 Salesforce.Com, Inc. Systems, methods, and apparatuses for implementing data upload, processing, and predictive query API exposure
US10860557B2 (en) 2013-03-13 2020-12-08 Salesforce.Com, Inc. Systems, methods, and apparatuses for implementing change value indication and historical value comparison
US20150142720A1 (en) * 2013-11-19 2015-05-21 Salesforce.Com, Inc. Predictive intelligence for service and support
US10311364B2 (en) * 2013-11-19 2019-06-04 Salesforce.Com, Inc. Predictive intelligence for service and support
US20180321683A1 (en) * 2017-05-02 2018-11-08 Cnh Industrial America Llc System and method for autonomous vehicle system planning
US10649457B2 (en) * 2017-05-02 2020-05-12 Cnh Industrial America Llc System and method for autonomous vehicle system planning
US11568281B2 (en) 2019-11-13 2023-01-31 International Business Machines Corporation Causal reasoning for explanation of model predictions

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WO1993021587A3 (fr) 1993-11-25

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