7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations1 Integration of Constraint-Based Reasoning and Case-Based Reasoning Mohammed H. Sqalli Eugene C. Freuder University of New Hampshire

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations2 Motivation l 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) l CBR completes the CSP model (Purvis 1998, Torasso 1998, Sqalli & Freuder 1998) l CBR corrects the CSP model (Sqalli & Freuder 1998)

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations3 Taxonomy Empirical (examplars)Analytic (models) Social system behavior Law Natural system behavior Artifact behavior Physics ComplexSimpleCompleteIncomplete/Incorrect Planning Physical systems Physics Interoperability testing   Branting 1998   Sqalli & Freuder 1998

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations4 Categorization of Modeling (Sqalli & Freuder 1998)

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations5 Domains of Application l Diagnosis l Configuration l Planning l Design

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations6 Good experiences l CADSYN (Maher & Zhang ): design constraints are used for adaptation l JULIA (Hinrichs 1992): a case-based meal planning system with a constraint propagator l CADRE (Hua & Faltings 1993): Constraints used to reduce the adaptation space l COMPOSER (Pu & Purvis 1995): solves problems using CSP for adaptation l CHARADE (Avesini, Perini & Ricci ): decision making in environmental emergencies l IDIOM (Smith & Faltings 1995): CSP for adaptation

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations7 Bad Experiences l It is hard to find in the literature such experiences since published papers usually include the successes and not the failures l 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)

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations8 Drawbacks l Integration tend to be domain oriented and may be applied to a limited domain theory (CBR limitation) l Overhead of switching from one reasoning method to the other l Time and Space limitations of each reasoning mode

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations9 l 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) l Solve large CSPs characterized by heavy searches (Huang & Miles 1996) l CBR: learning component (Sqalli & Freuder 1998) l Update the CSP model. Effectiveness of the model increases (Sqalli & Freuder 1998) l CBR used to solve DCSP (Purvis 1998) Advantages (CBR enhances CSP) CSP=Master, CBR=Slave

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations10 Advantages (CSP enhances CBR) CBR=Master, CSP=Slave l CBR adaptation process formulated as CSP (Purvis & Pu 1995) l Constraint-Based Adaptation for compensating incomplete cases. Cross-checking cases with constraints (Lee et al. 1997) l Add generic knowledge (CSP/ RBR) to cases (Bartsch- Sporl 1995) l Constraint-Based retrieval (Bilgic & Fox 1996) l Exploit the concept of interchangeability in CSP (Weigel, Faltings, & Torrens 1998) l Reduce number of cases used (Sqalli & Freuder 1998)

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations11 Trade-off l Overhead of using two modes of reasoning vs. limitations of each mode l Integration CBR/CSP can have advantages or may add more work l Adaptability criterion (Purvis 1998) l Updating models not for all domains l CBR/CSP: Space vs. Time l Balanced integration of CBR/CSP

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations12 What can we learn from other integrations ? l Approximate Model-Based Adaptation. Cases compensate for incompleteness in the model. Models compensate for insufficient case coverage [CARMA] (Branting 1998) l CBR contributes new links into the causality model (Karamouzis & Feyock 1992) l Best scenario: MBR + small number of cases (Torasso1998) l CBR accounts for errors in the model [ADAPtER] (Portinale & Torasso 1995) l CBR used as a form of caching to speedup later problem solving (Van Someren et al. 1997) l Unifying two modes (voting) better than combining them (Domingos 1998)

7/27/98 Sqalli & Freuder, AAAI-98 Workshop on CBR Integrations13 What did we learn ? l CSP provides a domain-independent representation of a task (adaptation in CBR) l CBR is useful for incomplete domains. Model is either difficult or impossible to get l CSP provides a rich representation of a task l CSP provides many advanced algorithms to deal with hard problems l CBR provides a very useful learning component