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Integration of Constraint-Based Reasoning and Case-Based Reasoning

Integration of Constraint-Based Reasoning and Case-Based Reasoning. Mohammed H. Sqalli Eugene C. Freuder University of New Hampshire msqalli,ecf@cs.unh.edu. Motivation. CSP used for the adaptation process in CBR:

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Integration of Constraint-Based Reasoning and Case-Based Reasoning

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  1. Integration of Constraint-Based Reasoning and Case-Based Reasoning Mohammed H. Sqalli Eugene C. Freuder University of New Hampshire msqalli,ecf@cs.unh.edu Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  2. Motivation • CSP used for the adaptation process in CBR: • Solve a problem when a complete knowledge of the domain is difficult to get (Weigel et al. 1998) • Achieve domain independence in adaptation (Purvis & Pu 1995) • Make solution space easier to explore (Smith & Faltings 1995) • CBR completes the CSP model (Purvis 1998, Torasso 1998, Sqalli & Freuder 1998) • CBR corrects the CSP model (Sqalli & Freuder 1998) Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  3. Taxonomy • Branting 1998 Empirical (examplars) Analytic (models) Social system behavior Natural system behavior Artifact behavior Law Physics • Sqalli & Freuder 1998 Complex Simple Complete Incomplete/Incorrect Physical systems Interoperability testing Planning Physics Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  4. Categorization of Modeling (Sqalli & Freuder 1998) Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  5. Domains of Application • Diagnosis • Configuration • Planning • Design Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  6. Good experiences • CADSYN (Maher & Zhang 1991-93): design constraints are used for adaptation • JULIA (Hinrichs 1992): a case-based meal planning system with a constraint propagator • CADRE (Hua & Faltings 1993): Constraints used to reduce the adaptation space • COMPOSER (Pu & Purvis 1995): solves problems using CSP for adaptation • CHARADE (Avesini, Perini & Ricci 1993-94): decision making in environmental emergencies • IDIOM (Smith & Faltings 1995): CSP for adaptation Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  7. Bad Experiences • It is hard to find in the literature such experiences since published papers usually include the successes and not the failures • There is one example showing that CSP/CBR integration may not be the best alternative: • Nutritional menus: CSP/CBR may not be the best way of solving this problem, because of the monotony of the solutions it provides. A CBR/RBR system seems to be a more suitable for these kinds of applications (Marling, Petot & Sterling 1998) Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  8. Drawbacks • Integration tend to be domain oriented and may be applied to a limited domain theory (CBR limitation) • Overhead of switching from one reasoning method to the other • Time and Space limitations of each reasoning mode Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  9. Advantages (CBR enhances CSP)CSP=Master, CBR=Slave • CSP solving efficiency improved when starting from a case rather than from nothing: • Fill values of CSP problem (Purvis & Pu 1995) • Reduce search space (Huang & Miles 1996) • Solve large CSPs characterized by heavy searches (Huang & Miles 1996) • CBR: learning component (Sqalli & Freuder 1998) • Update the CSP model. Effectiveness of the model increases (Sqalli & Freuder 1998) • CBR used to solve DCSP (Purvis 1998) Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  10. Advantages (CSP enhances CBR)CBR=Master, CSP=Slave • CBR adaptation process formulated as CSP (Purvis & Pu 1995) • Constraint-Based Adaptation for compensating incomplete cases. Cross-checking cases with constraints (Lee et al. 1997) • Add generic knowledge (CSP/ RBR) to cases (Bartsch-Sporl 1995) • Constraint-Based retrieval (Bilgic & Fox 1996) • Exploit the concept of interchangeability in CSP (Weigel, Faltings, & Torrens 1998) • Reduce number of cases used (Sqalli & Freuder 1998) Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  11. Trade-off • Overhead of using two modes of reasoning vs. limitations of each mode • Integration CBR/CSP can have advantages or may add more work • Adaptability criterion (Purvis 1998) • Updating models not for all domains • CBR/CSP: Space vs. Time • Balanced integration of CBR/CSP Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  12. What can we learn from other integrations ? • Approximate Model-Based Adaptation. Cases compensate for incompleteness in the model. Models compensate for insufficient case coverage [CARMA] (Branting 1998) • CBR contributes new links into the causality model (Karamouzis & Feyock 1992) • Best scenario: MBR + small number of cases (Torasso1998) • CBR accounts for errors in the model [ADAPtER] (Portinale & Torasso 1995) • CBR used as a form of caching to speedup later problem solving (Van Someren et al. 1997) • Unifying two modes (voting) better than combining them (Domingos 1998) Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

  13. What did we learn ? • CSP provides a domain-independent representation of a task (adaptation in CBR) • CBR is useful for incomplete domains. Model is either difficult or impossible to get • CSP provides a rich representation of a task • CSP provides many advanced algorithms to deal with hard problems • CBR provides a very useful learning component Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations

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