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TOWARDS A COMPONENT- BASED PLATFORM FOR DEVELOPING CBR SYSTEMS JOSE MARIA ABASOLO LLUCH Directores Enric Plaza Josep-Lluis Arcos Tutor Ton Sales.

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Presentation on theme: "TOWARDS A COMPONENT- BASED PLATFORM FOR DEVELOPING CBR SYSTEMS JOSE MARIA ABASOLO LLUCH Directores Enric Plaza Josep-Lluis Arcos Tutor Ton Sales."— Presentation transcript:

1 TOWARDS A COMPONENT- BASED PLATFORM FOR DEVELOPING CBR SYSTEMS JOSE MARIA ABASOLO LLUCH Directores Enric Plaza Josep-Lluis Arcos Tutor Ton Sales

2 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

3 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

4 Motivation Case Base Reasoning deals with solving new problems from the experience of previous solved problems Case Base Reasoning uses Case Knowledge (previous experience) General Knowledge of the application domain Development of CBR systems involves decisions about CBR techniques Case representation Two perspectives on CBR systems Scientific engineer Application engineer

5 Motivation The scientific engineer develops and characterizes CBR techniques The application engineer develops CBR systems Would like to reuse previous CBR developments Goal: Support both perspective to improve the development of CBR systems

6 Approach CBR techniques Knowledge components Knowledge components reusability Developing CBR system Configuring among the Knowledge components Component Description Language to characterize CBR techniques Domain independent platform to support the development of CBR systems

7 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

8 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

9 CBR New case New Case Retrieved cases Solved case Repaired case Learned case Precedent cases Domain knowlegde Retrieve Reuse Revise Retain Problem (Aamodt &Plaza 94) Confirmed solution Suggested solution

10 CBR Problem space Solution space A R

11 Previous Works [Aamodt&Plaza94] propose to decompose the different processes in subtasks and methods [Armengol97] introduces the Task-Method decomposition for Machine Learning techniques introduces Methods to acquire knowledge dynamically [Diaz02] proposes the development of KI-CBR systems using Description Logics Ontology Tasks and Methods

12 CBR Development Methodologies INRECA is a methodology that provides the guidelines for the activities to succesfully develop CBR systems Characterize Set Goals Choose Process Execute Analyze Package CBR-PEB is a decision support system for CBR system development Support for feasibility study Support for evaluating the applicability of techniques Support for a state-of-the-art study

13 CBR Shells CBR-Works Structured cases Predefined Similarity measures and similiratiy measures editor Adaptation Rules (if condition then action) K-commerce Not structured cases and problems with unknown values Not general knowledge representation Support large case bases Retrieve and Retain KATE NN for Retrieval Personalized similarity measures Combines NN with dynamic induction Data Mining module to acquire knowledge ReMind Not structured cases and problems with unknown values Retreival (NN, induction of DT and Query Database) Reuse Rules (if condition then action) Retain ReCall Combines NN and Inductive methods Allows structured cases, incomplete cases and uncertain knowlewdge

14 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

15 Knowledge Modelling A knowledge modelling framework defines the basic types of modelling components, their relations,and proposes a model development methodology. Task-Method-Decomposition Approaches: Generic Tasks Components of Expertise KADS and CommonKADS Problem-Solving Methods UPML

16 Generic Tasks Chandresekaran presents a methodology of developing KSs oriented by Tasks. A Task is described based on the Methods that can solve it and the requisits of this methods to be applied. A Method is described based on the knowledge used and the decomposition into subtasks.

17 Components of Expertise Steels proposes a framework of describing experience at a knowledge level This knowledge level focuses on the knowledge of the experience more than the implementation structures. The experience is decomposed in: Tasks: specifies the goal and the decomposition into subtasks Models: the knowledge needed to solve the tasks Case Model (specific situtation solving tasks) Domain Model (concrete knowledge about domain) Methods: specifies where and when knowledge is applied Decomposers Execution

18 KADS and CommonKADS KADS is a methodology for analysis and design of KSs CommonKADS = KADS + Components of Expertise Models capturing different aspects of the KS Organization Model Task Model Agent Model Communication Model Design Model Expertise Model Domain Layer Inference Layer Task Layer

19 Problem-Solving Methods Common patterns in the reasoning processes in KSs. (e.g. Generate&Test, Propose&Revise) These patterns can be described independently from the application domain reusability. Two types of PSMs: Decomposing a task into subtasks Primitive Libraries of PSMs

20 UPML UPML is a software architecture, that tries to encapsulate some of the previous approaches UPML deals with the objective of defining components independently of the domain using ontologies for describing components. UPML has two main drawbacks for our purpose UPML works statically with domain models. UPML specifies the components using some formula that do not take into account characterisitics on the specific domain model. TaskPSM Domain model PSM-Task Bridge Task-DM Bridge PSM-DM Bridge Ontologies

21 Context Currently KM approaches converge to a task- method-decomposition approach BUT CBR system development does not use KM approaches KM has some features that are not suitable for CBR systems development Our proposal is to develop, upon the idea of [Aamodt&Plaza94] of decomposing the different processes into subtasks and methods, a component based platform to develop CBR systems

22 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

23 Component Description Language Task PSM Task-PSM matching PSM-Domain matching Domain Model Content

24 Component Description Language A Task is specified by: Goals to be achieved Preconditions need to be satisfied Input roles Output roles CDL allows Tasks which output is a Domain Model. R i Input roles R o Output roles P Preconditions G Goals Task PSM Task-PSM matching PSM-Domain matching Domain Model Content

25 Component Description Language PSM (Problem-solving method) that characterizes the way to solve a task: Competence: goals the PSM is able to achieve Preconditions: properties that need to be satisfied for the PSM to be applicable Input and Output roles Knowledge roles: types of Domain Models Assumptions: properties of the Domain Models that are assumed to hold R i Input roles R o Output roles P Preconditions C Competence R k Knowledge Roles AAssumption Task PSM Task-PSM matching PSM-Domain matching Domain Model Content

26 Component Description Language Task PSM Task-PSM matching We distinguish three types of PSM PD I O OD TT DM RR I O DM E I PSM-Domain matching Domain Model Content

27 Component Description Language Domain Model characterizes domain knowledge that is used by PSM to solve tasks: - Type of knowledge content - Properties satisfied by the knowledge content - Assumptions: properties assumed to be satisfied by the knowledge content We distinguish between the knowledge content (expressed in the domain ontology) and characterization of the content (expressed in the Task-PSM ontology). S Type of the knowledge content P Properties A Assumptions Task PSM Task-PSM matching PSM-Domain matching Domain Model Content

28 Component Description Language Task-PSM matching Task-P PSM-P PSM-C Task-G Task PSM Task-PSM matching PSM-Domain matching Domain Model Content

29 Component Description Language PSM-Domain matching Task PSM Task-PSM matching DM-P U DM-A PSM-A PSM-Domain matching Domain Model Content

30 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

31 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

32 CBR ontology The CBR ontology defines the concepts used to characterize the Tasks, PSMs and Domain Models for CBR systems. The preconditions, assumptions, goals and competence of the components are specified using the CBR ontology The ontology contains concepts to describe Noise Tolerance Accuracy Classification-Variability Difference-Bias Time and Space cost Characteristics of the retrieved cases …

33 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

34 CBR Models We have defined a typology of CBR Models The different roles (input, output and knowledge) of the Tasks, PSMs and Domain Models are typed using these CBR Models Some of these CBR Models are: Case-Collection Case-Base Similarity-Model (Case-Similarity-Model, Grouped-Model, Set-of- cases) K-Model (Unique-K, K-Case-Model, K-Class-Model) Weight-Model Order-Model Decision-Tree-Model

35 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

36 CBR components The CBR Library include components for: – Retrieval – Nearest Neighbor, Decision Tree indexing – Subsumption Mechanisms (perspectives) – Similarity – Feature similarity (numbers, ordered labels, …) – Structured cases similarity (SHAUD) – Aggregation (Weighted mean, euclidian, city-block, OWA, WOWA) – Reuse – Classification (Voting, selection criteria) – Configuration (Constructive Adaptation techniques) – Model Acquisition (Enabling) – Decision Tree induction – Weight models (gain, RLDM), numerical discretization – K-models

37 An example of PD Problem DecomposerK-NN-Retrieval OntologyCBR-Ontology Input rolestarget : CBR-Case Output rolescases : CBR-Case SubtasksAsses similarity Select-k-neighbours Preconditions CompetenceRetrieve-Similar-cases-with- similarity Operational Description(use-subtask Select-k-neighbours target (use-subtask Assess-Similarity target))

38 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

39 CAT-CBR Platform User Requirements

40 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

41 CAT-CBR Configuring - The configuring process has been treated as a Search process in a space of states. - These states represent a partial configuration of a CBR system. Configuring Process User Requirements PreconditionsInputs GoalsDomain-Models CBR LibraryUser Requirements Configured CBR system CBR system configured Task PSM PSM Knowledge Role DM

42 Task-method decomposition Task PSM Subtasks

43 Configuring a Task-method decomposition PSMs DM

44 CAT-CBR Configuring Configuring Process CBR LibraryUser Requirements Configured CBR system

45 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

46 CAT-CBR Enabling The goal of the enabling process is to connect the configured CBR system with a specific domain of application Configured CBR system Enabling Process Enabled CBR system Drums-Model-Instrument Drums-cases k-Model- for-Drums Generate-k-Model Global-k Drums-cases Drums-Model-Instrument

47 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

48 CAT-CBR Enacting The Enacting process links the enabled configuration with the PSM operationalizations (use-subtask Select-k-Neighbours target) (define-method K-selection :inputs ((target CBR-Case)) :domains ((k-Model Unique-k) (Case-Language-Model CLM)) method-code) (K-selection :inputs (targets) :domains (k-Model-for-drums Drums-Model-Instrument)) Enabled CBR system Enacting Process Enacted CBR system Select-k-Neighbours K-selection Drums-Model-Instrument k-Model-for-drums

49 CAT-CBR Enacting CBR system enabled Configuring Process CBR system enacted CBR Ontology Domain Ontology Case Language Model

50 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

51 Application Domain Audio Content Description Classification Automatic labelling of sounds sampling and synthesis devices Application Domain = Unpitched percussion sounds There are many sound descriptors Different source recordings Different criteria of classification (instrument, family) A sound is described using 207 attributes There are 30 instrument classes

52 Configuring CBR system Configuring Process User Requirements Preconditions Propositonal-Case Inputs(Problem CBR-Case) Goals Classify-Problem Domain-Models (Case-Base-Model Propositional-Case No-Missing-Values) (Case-Language-Model) CBR LibraryUser Requirements

53 Configuring CBR system Configuring Process CBR Library User Requirements Configured CBR system Classify Classify-by-k-NN Retrieve-for-k-NN Reuse-for-k-NN K-NN-Retrieval Assess-Similarity Feature-Similarity-and-Aggragation Feature-Similarity Feature-Similarity-No-Missing-Values Case-Base-Model Case-Language-Model Aggregation City-Block-Aggregation K-Model Case-Language-Model Select-k-Neighbours K-selection Majority Case-Language-Model

54 Enabling CBR system Configured CBR system Enabling Process Enabled CBR system Classify Classify-by-k-NN Retrieve-for-k-NN Reuse-for-k-NN K-NN-Retrieval Assess-Similarity Feature-Similarity-and-Aggragation Feature-Similarity Feature-Similarity-No-Missing-Values Case-Base-Model Case-Language-Model Aggregation City-Block-Aggregation K-Model Case-Language-Model Select-k-Neighbours K-selection Majority Case-Language-Model Drums-cases Drums-Model-Instrument k-Model-for-drums Generate-k-Model Global-k Drums-cases Drums-Model-Instrument

55 Enacting CBR system (define (Case-Language-Model :id Drums-Case-Language) (case-spec (define-spec (Mostpercussionclasses))) (case-attributes Specflat Speccent Speccentn Strpeak Kurtosis Scr Zcr Strdecay Varsc Varzcr Skew B1 B1bis B2 B2bis B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13....) (solution-attribute Instrument) (solution-accessor Instrument-Accessor)) Case structure definition Selected Attributes (define (Numeric-Attribute :id Specflat) (name 'Specflat) (accessor Specflat-Accessor) (sim-function 'Specflat-Similarity) (range (define (interval) (inf-lim -61) (sup-lim -1))))

56 Testing CBR systems Classify Classify-by-k-NN Retrieve-for-k-NN Reuse-for-k-NN K-NN-Retrieval Assess-Similarity Feature-Similarity-and-Aggragation Feature-Similarity Feature-Similarity-No-Missing-Values Case-Base-Model Case-Language-Model Aggregation City-Block-Aggregation K-Model Case-Language-Model Select-k-Neighbours K-selection Majority Case-Language-Model Classify Classify-by-k-NN Retrieve-for-k-NN Reuse-for-k-NN K-NN-Retrieval Assess-Similarity Feature-Similarity-and-Aggragation Feature-Similarity Feature-Similarity-No-Missing-Values Case-Base-Model Case-Language-Model Aggregation Euclidean-Aggregation K-Model Case-Language-Model Select-k-Neighbours K-selection Majority Case-Language-Model

57 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

58 Conclusions Reusability of CBR techniques across different domains Task-Method decomposition of CBR techniques We have defined a CDL addresing the needs of the CBR development CBR Library that includes a representative set of tasks and PSMs for the Retrieve and Reuse phase We do not pretend to cover all CBR techniques, we only present some classic methods and viability of define and specify new methods using the CDL CAT-CBR platform to develop CBR systems We have presented the development process of a CBR system as a three phases processes ConfigureEnableEnact

59 Future Work To incorporate other techniques to the CBR Library Knowledge Intensive CBR (CREEK techniques) Higher Order Retrieval techniques (perspectives) Reuse techniques for Configuration (Constructive Adaptation) To further study two application domains Device configuration Music content retrieval and analysis

60 OUTLINE Motivation and approach State of the art CBR Knowledge Modelling A Component Description Language CBR Library CBR Ontology CBR Models CBR Tasks and PSMs The CAT-CBR platform Configuring Enabling Enacting An example of using CAT-CBR Conclusions and Future Work Publications

61 M. Gomez, C. Abasolo, E. Plaza (2003), Open, Reusable and Configurable Multi-Agent Systems. Third price in the AgentCities Technology ATC03, in the Infrastructures category. M. Gomez, C. Abasolo (2003), A general framework for meta-search based on query weighting and numerical aggregation operators. In Bouchon-Meuner, B. Foulloy, L and Yager, R.R. (eds.). Intelligent Systems for Information Pro-cessing: From Representation to Applications, pp , Elsevier Science. C. Abasolo, E. Plaza, J. Arcos (2002),Components for Case-Based Reasoning. In Proc.Congres Catala d'Intel.ligencia Artificial, CCIA M. Gomez, C. Abasolo, E. Plaza (2002), Problem-solving Methods and Cooperative Information Agents. In Proc. Special Issue of the International Journal on Cooperative Information Systems vol. 11(3), M. Gomez, C. Abasolo (2002), Improving meta-search by using query-weighting and numerical aggregation operators. In Proc. Information Processing and Management of Uncertainty conference, IPMU C. Abasolo, M. Gomez (2002), A framework for meta-search based on numerical aggregation operators. In Proc.Congres Catala d'Intel.ligencia Artificial,CCIA M. Gomez, C. Abasolo, E. Plaza (2001), ·Domain-independent ontologies for cooperative information agents·. In Proc. Fifth International Workshop Cooperative Information Agents CIA Lecture Notes in Artificial Intelligence, LNAI 2128, p Springer-Verlag. C. Abasolo, M. Gomez E. Plaza (2001), Agents d'informacio independents del domini. In Proc.Congres Catala d'Intel.ligencia Artificial, CCIA J. M. Abasolo, M. Gomez (2000). MELISA: An ontology-based agent for information retrieval in medicine. Proceedings of the First International Workshop on the Semantic Web (SemWeb2000). Lisbon, Portugal, pp

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