Presentation is loading. Please wait.

Presentation is loading. Please wait.

Copyright R. Weber Case-based reasoning ISYS 370 R. Weber.

Similar presentations

Presentation on theme: "Copyright R. Weber Case-based reasoning ISYS 370 R. Weber."— Presentation transcript:

1 Copyright R. Weber Case-based reasoning ISYS 370 R. Weber

2 Copyright R. Weber CBR applications CCBR conversational CBR

3 Copyright R. Weber Deployed CBR applications (i) PROFIT valuates residential properties to evaluate mortgage packages for a division of GE Mortgages. Values of a property change with market conditions, so estimates have to be updated constantly according to real estate transactions, which validate the estimations. CARMA is designed to provide expert advice on handling rangeland grasshopper infestations. CARMA has reused its expertise combined with model-based methods to devise policies on pest management and the development of industry strategies.

4 Copyright R. Weber Deployed CBR applications (ii) General Motors has developed an organizational CBR system to support the goals of dimensional management, an area in the manufacturing of mechanical structures (e.g., vehicle bodies) that enforces quality control by reducing manufacturing variations that occur in fractions of millimeters. Western Air is an Australian distributor of heat and air conditioning systems; they have chosen to use a web- based CBR application [20] to guarantee a competitive advantage that also poses an entry barrier to competition. They guarantee the precision of the specifications of each new system and the accuracy of the quotes by relying in knowledge captured in previous installations.

5 Copyright R. Weber Deployed CBR applications (iii) Dublet recommends apartments for rental in Dublin, Ireland, based on a description of the users preferences. It employs information extraction from the web (of apartments for rent) to create cases dynamically and retrieves units that match the users preference. Dublet performs knowledge synthesis (creation) and extends the power of knowledge distribution of the CBR system by being operational in cell phones. PTV combines case-based (content-based) personalization with collaborative filtering to recommend shows to watch on digital television.

6 Copyright R. Weber Deployed CBR applications (iv) NEC has developed SignFinder, which is a system that detects variations in the case bases generated automatically from customer calls. When they detect variations on the content of typical customers requests, they can discover knowledge about defects on their products faster than with any other method.

7 Copyright R. Weber nametaskauthorobs. ABBYRomantic advisor; retrieves a similar history DomeshekSocial context ALFAPredict power demandJabourSame result but faster than human experts ARCHIE ARCHIE 2 Architecture design of office buildings Goel, Kolodner and Domschek CADETDesign of mechanical components Sycara, Navinchandra Abstract indexing allowed innovative design CASEY Diagnosis cause and prescribes solution to heart problems Kotonmodel-based Compaq SMART Diagnosis and repair; customer support help desks Acorn, Walden Uses Inferences tool; can be used by up to 60 users at a time; shows that library engineering is necessary CHEFDesign of recipes to meet different simultaneous goals Hammond case-based planning: Memory started with 20 recipes and learned from user feedback CLAVIERDesign and evaluation of autoclave loading Barletta & Hennessy Interacts planning and scheduling COACHPlanning soccer gamesCollinsDebugging and fixing bad strategies; memory keeps strategies and the type of problem HYPOInterpretation and argumentation Rissland & Ashley Retrieves similar cases to create a point, a response, and a rebuttal using hypotheticals (Ashley, 1990) JUDGE Defines sentences of delinquent crimes based on the chances of repeating the crime and its severity BainIn case of not having a sufficient similar case, the system uses heuristics to determine the sentence JULIAplanning mealsHinrichsPlausible reasoning and design

8 Copyright R. Weber nametaskauthorobs. MEDIATO R Mediates conflicts by performing planning SimpsonKeeps in memory failed solutions and tries to avoid same failures in new solutions PERSUADE R Mediation of union negotiations; proposes solutions with arguments SycaraConsiders parts goals and considers recent accepted solutions AMADEUSsuggests how to write papers Aluisio, 1995 PLEXUSPlanning daily tasksAltermanAdapts the experience of riding the SF metro to reuse in NY PRODIGYPlanning and learningVeloso, Carbonell Demonstrated in a variety of domains PROTOSHeuristic classification for diagnosis Bareiss, Porter, Murray, Weir, Holte Automatic knowledge acquisition; good for weak theory domains SQUADSoftware quality control advisor Kitano20,000 cases in 1993 SWALEGenerates explanation of anomalous events in news stories Schank, Kass, Leake, Owens Searches for similar explanations for death and destruction such as the murdered spouse that was killed because of the insurance money just like the horse (SWALE) that was killed by its owner for the same reason Mostly from Kolodner 1993

9 Copyright R. Weber

10 nametaskauthorobs. CATOTutoring systemAleven/Ashle y Teaching law students to create argument HVAC systemTests and diagnosis of faults in A/C systems Watson, 2000Diagnosis and solutions to HVAC maintenance Operated by salespersons Western Australia The Auguste Project CBR is used to decide whether a patient benefits from a drug and RBR decides which drug to choose Marling 2001Planning ongoing care for AD (Alzheimer) cases based on strategies that worked better in past cases HICAPCase-based planningMunoz Avila 1999 Combines case-based planning with methods in planning NEOs PRUDENTIAJurisprudence research; textual CBR Weber, 1998Case retrieval FormToolCBR in color matchingCheethamGE CRD Savings of 2.25 million per year in productivity and cost reduction DUBLETRecommends rental properties from different online sources Hurley, Wilson 2001 Is used on the web and in mobile phones Employs Information Extraction tools to gather info from the web- returns properties ranked according to similarity PTV (personalized TV listings) Each user receives a daily personalized TV listing specially compiled to suit each users individual preferences Cotter & Smyth Cbr and collaborative filtering CF makes a recommendation to a person because his or her profile is similar to other people who have chosen the recommended item. Recent applications Springer series on CBR Research and Development

11 Copyright R. Weber Further reading Riesbeck & Schank (1989) Inside case-based reasoning Kolodner (1993) Case-based reasoning Aamodt & Plaza (1994) AICom paper (todays reading) Leake (1996) Leake, David. (1996). Case-Based Reasoning: Experiences, Lessons, and Future Directions. Watson (1997) Applying Case-Based Reasoning: techniques for enterprise systems.

12 Copyright R. Weber Introduction from a knowledge representation concept (i.e. scripts, MOPS) role of understanding in solving problems CBR assumptions: –similar problems have similar solutions –problems recur (Leake, 1996)

13 Copyright R. Weber Definitions From Riesbeck & Schank (1989), "A case-based reasoner solves new problems by adapting solutions that were used to solve old problems". Case-Based Reasoning systems mimic the human act of reminding a previous episode to solve a given problem due to the recognition of their affinities (Weber, 98). Case-based reasoning is a methodology that reuses previous episodes to approach new situations. When faced with a new situation, the goal is to retrieve a similar previous one and reuse its strategy (Weber, 02).

14 Copyright R. Weber CBR methodology Task? AI Task: Diagnosis Prescription Interpretation-advice Recommendation Analysis-prediction Schedule Planning

15 Copyright R. Weber case base case representation CBR methodology Task?

16 Copyright R. Weber CBR methodology case base situation assessment

17 Copyright R. Weber CBR methodology case base RETRIEVE REUSE REVISE RETAIN

18 Copyright R. Weber Knowledge in case- based reasoning systems by Richter, M. M., The Knowledge Contained in Similarity Measures: Some remarks on the invited talk given at ICCBR'95 in Sesimbra, Portugal, October 25, 1995. Online: http://www.cbr- r95remarks.html

19 Copyright R. Weber Case representation case problem: symptoms A, B, C case solution: disease 1 case outcome: confirmed

20 Copyright R. Weber Case acquisition/authoring cases are acquired from real experiences cases are created from categories of real experiences (prototypes) cases are authored by an expert cases are learned by data analysis cases are searched in patterns cases are converted (extracted) from text cases are learned from text

21 Copyright R. Weber Similarity The key to its success is expertise to determine what makes a case similar to another. For example, if you have a common cold and your spouse has the flu, you will be able to recognize these two conditions are similar. But only a physician can determine whether two infirmities are similar so that the same treatment can be applied. It is expert knowledge that tells when a case is similar to another in the context of a CBR system. Similarity function is a knowledge representation formalism to measure similarity between two cases

22 Copyright R. Weber Retrieval similarity functions measure similarity all cases (or a selected portion) are compared to the target (problem) case cases are retrieved when their similarity is above a pre-defined threshold this threshold determines the point from which cases are considered similar

23 Copyright R. Weber Adaptation All features that describe a case and are not used for retrieval can potentially be adapted

24 Copyright R. Weber Adaptation methods substitution –reinstantiation: replacement based on a role –parameter adjustment (proportional) –local search (taxonomy) –query memory –case-based substitution: alternatives in cases transformation: transform by changing features either by substitution or deletion –common-sense transformation –model-guided repair

25 Copyright R. Weber Learning learning by incorporating new cases to the case base learning by adding cases that are adaptations from retrieved cases

26 Copyright R. Weber CBR and AI tasks (i) interpretive: –past cases are used as references to categorize and classify new cases –interpretation, diagnosis problem-solving –past cases are used to provide a solution to be applied to new cases –design, planning, explanation

27 Copyright R. Weber CBR and AI tasks (ii) Mundane prediction-advice composition understanding reading planning walking uncertainty creativity Both interpretation classification categorization discovery control monitoring learning planning analysis explanation Expert diagnosis- troubleshooting prescription configuration design scheduling retrieval mediation argumentation recommendation

28 Copyright R. Weber vocational counseling diagnosing headaches

29 Copyright R. Weber Advantages of CBR systems (i) Knowledge acquisition and representation: There is no need to explicit acquire and represent all the knowledge the system can use. CBR systems can avoid mistakes Common sense: knowledge that would have to be represented explicitly is implicitly stated in cases. Not easily formalizable tasks: such as in some medical domains, prototypical descriptions represent more easily a body of knowledge.

30 Copyright R. Weber Advantages of CBR systems (ii) Creativity - Case solutions can be combined into new ones and cases can also be used in a different level of abstraction providing innovative solutions. Learning - can be done without human interference; CBR systems can become robust and provide better solutions. Users feedback is easily incorporated in the revise phase. Degradation -CBR systems can recognize when no answer exists to a problem by simply defining a threshold from which a solution is no longer acceptable. In decomposable problem domains, a solution can be created from the combination of partial solutions.

31 Copyright R. Weber Advantages of CBR systems (iii) (shared with ES and other AI methods) Permanence - CBR do not forget unless you program it to. Breadth - One CBR system can entail knowledge learned from an unlimited number of human experts. Reproducibility - Many copies of a CBR system.

32 Copyright R. Weber current issues case authoring case base maintenance methods for distributed case bases

33 Copyright R. Weber Building (shells), using, maintaining Shells/tools – –Esteem examples, NISTP CBR Shell examples Using –Laypeople, experts Maintaining –Automatically learning new cases Cases are real or created –Manually adding new cases

34 Copyright R. Weber CBR and grounds for computer understanding Ability to represent knowledge and reason with it. Perceive equivalences and analogies between two different representations of the same entity/situation. Learning and reorganizing new knowledge. –From Peter Jackson (1998) Introduction to Expert systems. Addison-Wesley third edition. Chapter 2, page 27.

Download ppt "Copyright R. Weber Case-based reasoning ISYS 370 R. Weber."

Similar presentations

Ads by Google