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CBR matching & brokering

This document outlines the integration of the UPML (User and Process Meta-Language) framework into Case-Based Reasoning (CBR) systems, focusing on memory retrieval and component matching strategies. It discusses the role of brokers in generating efficient queries based on user consultations, utilizing subsumption filtering to match problem-solving methods (PSMs) with task conditions. The S&S Broker strategies are analyzed to improve configuration search within CBR applications, ultimately balancing memory usage with search effectiveness for complex configurations.

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CBR matching & brokering

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  1. CBR matching & brokering IIIA-CSIC IBROW

  2. Framework • UPML components as cases • Retrieved by CBR constructs in NOOS • UPML meta-ontology • Object language • Describes CBR properties • Concept language • Feature logics • Similar to description logics (aka <OIL>)

  3. Component matching • UPML components are cases in memory • We can query the memory • Subsumption (query ≤ case) • How do we generate queries? • Configuration program (“Broker”) • Receives a “user consult” for a system • Generates queries for particular components in memory • Classical search space program • Knows about UPML

  4. Matching • Consult: precond+postcond+models+I/O • For a given task T • Retrieve PSM (CBR subsumption retrieve): • T.postcond ≤ PSM.postcond • Filter retrieved PSMs (subsumption filtering) • PSM.precond ≤ T.precond • Input & Outputs of T & PSM match • Goal: • find configuration (state) where consult is satisfied

  5. S&S Broker • Broker understands 2 things • UPML • Object language • Broker is a P. Solver for OL • OL=constraints => Broker=SAT solver • OL= FOL => Broker=theorem prover • S&S broker • OL = language for cases in CBR • Broker= search technique

  6. Components for CAT-CBR

  7. Case-base of configured apps

  8. Search & Subsume Broker • Search & Subsume Broker • Search thru state space • State represents properties of a partially instantiated configuration • UPML: understands goals & assumptions • Concept language: • Subsumption of concept expressions

  9. S&S Broker strategies • Depth first (shown Stanford) • S&S Broker • Best first (shown here) • CBR Broker • Sorting = case-based similarity

  10. Goals Assumptions Knowledge Met-goals Met-assumptions Open-knowledge Used-knowledge PSM-hypothesis Open-tasks Tasks PSMs State

  11. A sepecification of state

  12. A CONFIGURATION CASE

  13. Knowledge for search • Initial-state: consult->state • Successor: State-> State s • Combiner: States->Ordered- States • Case-based similarity to order states • Else order by depth or breadth • Goal-state: State ->Boolean • Finalize: State->Configuration

  14. A configuration

  15. New state, new hypothesis • N possible PSMs for a task • N new hypothesis, N ne w states

  16. CBR configuration as BestFirst

  17. Specifying a CBR application

  18. Configured CBR application

  19. Conclusions • CBR balances memory vs. search • New configuration is also a case • With “compiled” goals & assumptions • S&S Broker can retrieve config • (config = case) • Separate configuration from requirement acquisition • CBR broker: adapting “similar” configs to user-posed problems

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