1 / 16

Knowledge-Intensive Case-Based Reasoning in CREEK

Knowledge-Intensive Case-Based Reasoning in CREEK. Knowledge-Intensive CBR in. Introduction. Assumes cases enriched with general domain knowledge Focus in these types of cases Study CREEK architecture and system (in general terms). Knowledge-Intensive CBR in.

nia
Download Presentation

Knowledge-Intensive Case-Based Reasoning in CREEK

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Knowledge-Intensive Case-Based Reasoning in CREEK

  2. Knowledge-Intensive CBR in Introduction • Assumes cases enriched with general domain knowledge • Focus in these types of cases • Study CREEK architecture and system (in general terms)

  3. Knowledge-Intensive CBR in The Knowledge-Intensiveness dimension of CBR methods MBR IBL/IBR CREEK

  4. Knowledge-Intensive CBR in What is knowledge in a CBR system? • Difficult to distinguish knowledge concept from information or data. • Attemps: • 1) Based on size or complexity (fail) • 2) “CREEK” option: • How and for What purpose the structures are used.

  5. Knowledge-Intensive CBR in Knowledge Learning Information Data The Data-Information-Knowledge model Knowledge is learned information Elaboration Information is interpreted data Data Interpretation Data are syntactic entities whitout meaning for the system.

  6. Knowledge-Intensive CBR in Case roles in CBR systems • Knowledge is needed for a system to reason. • (Interpreting data and deriving new info) • Roles of Cases: • 1) Cases as data • 2) Cases as information • 3) Cases as Knowledge • CB system architecture can facilitate a gradual transformation from a pure database or information system, to a full-fledged knowledge-based system.

  7. Knowledge-Intensive CBR in Symbol Level Design and Implementation Knowledge Level Analysis and Modeling Sustained Learning Periodic Knowledge Revision EXPERIENCE COMPUTERINTERNALMODEL MENTAL MODEL CONCEPTUAL KNOWLEDGE MODEL Problem Solving New Case The Knowledge modeling cycle

  8. Knowledge-Intensive CBR in The CREEK system • Is an architecture for knowledge-intesive case-based problem solving and learning, targeted at addressing problems in open weak-theory domains • It has different modules, the main ones are: • Object-level domain knowledge model • Strategy level model • Two reasoning meta-level models • It integrates problem solving and learning

  9. Knowledge-Intensive CBR in Solved problems + traces New problem Problem Solving Sustained Learning MBR EBL CBR CBL General Domain Model Solved cases The CREEK system Diagnosis and Repair Strategy Combined Learning Combined Reasoning

  10. Knowledge-Intensive CBR in The CREEK system • CBR tasks: • Retrieve • Reuse • Revise • Retain

  11. Knowledge-Intensive CBR in The CREEK system • The case-based interpreter in CREEK contains three-step process: • Activating ( Activation Strength > Threshold ) • Generating and Explaining • Focusing

  12. Knowledge-Intensive CBR in The CREEK system • Activate  determines a relevant broad context for the problem • Explain  will attempt to improve the match between the input case and the activated cases. • Focus  selects the best case or rejects all of them

  13. Knowledge-Intensive CBR in The CREEK system • Activation Strength is based on the number of matched relations and their relevance factor:

  14. Knowledge-Intensive CBR in The CREEK system • The general domain knowledge is good enough to provide support to the case-base methods and also back-up if no similar case is found • Troll Creek allows running the case matching process at any time during system development

  15. Knowledge-Intensive CBR in Recent and ongoing research • From Lisp implementation to Java • Revision of the knowledge representation and basic inference methods

  16. Knowledge-Intensive CBR in Conclusion • Knowledge-intensive CBR is at the core of the CREEK framework, architecture, and system. • The current and future in CREEK: • - methods related to combined explanatory power of general domain knowledge and cases

More Related