Case-based reasoning

1. Case-based reasoning INFO 629 R. Weber

2. Outline • introduction, definition • the concept and methodology • CBR cycle and its steps • CBR and AI tasks • applications • Building (shells), using, maintaining • Current issues • advantages/disadvantages • CBR and grounds for computer understanding

3. 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)

4. 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).

5. case representation CBR methodology Task? case base

6. CBR methodology situation assessment case base

7. CBR methodology RETRIEVE case base RETAIN REUSE REVISE

8. 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-web.org/documents/Richtericcbr95remarks.html

9. Case representation • case problem: symptoms A, B, C • case solution: disease 1 • case outcome: confirmed

10. 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

11. 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

12. 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

13. Adaptation • All features that describe a case and are not used for retrieval can potentially be adapted

14. 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

15. Learning • learning by incorporating new cases to the case base • learning by adding cases that are adaptations from retrieved cases

16. 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

17. 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

18. CBR applicationsCCBRconversational CBRhttp://www.egain.com/pages/Level2.asp?SectionID=4&PageID=4http://support.lucasarts.com/yoda/start.htm

19. 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.

20. 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.

21. Deployed CBR applications (iii) • Dublet recommends apartments for rental in Dublin, Ireland, based on a description of the user’s preferences. It employs information extraction from the web (of apartments for rent) to create cases dynamically and retrieves units that match the user’s 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.

22. 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.

23. name task author obs. ABBY Romantic advisor; retrieves a similar history Domeshek Social context ALFA Predict power demand Jabour Same result but faster than human experts ARCHIE ARCHIE 2 Architecture design of office buildings Goel, Kolodner and Domschek CADET Design of mechanical components Sycara, Navinchandra Abstract indexing allowed innovative design CASEY Diagnosis cause and prescribes solution to heart problems Koton model-based Compaq SMART Diagnosis and repair; customer support help desks Acorn, Walden Uses Inference’s tool; can be used by up to 60 users at a time; shows that library engineering is necessary CHEF Design of recipes to meet different simultaneous goals Hammond case-based planning: Memory started with 20 recipes and learned from user feedback CLAVIER Design and evaluation of autoclave loading Barletta & Hennessy Interacts planning and scheduling COACH Planning soccer games Collins Debugging and fixing bad strategies; memory keeps strategies and the type of problem HYPO Interpretation 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 Bain In case of not having a sufficient similar case, the system uses heuristics to determine the sentence JULIA planning meals Hinrichs Plausible reasoning and design

24. name task author obs. MEDIATOR Mediates conflicts by performing planning Simpson Keeps in memory failed solutions and tries to avoid same failures in new solutions PERSUADER Mediation of union negotiations; proposes solutions with arguments Sycara Considers part’s goals and considers recent accepted solutions AMADEUS suggests how to write papers Aluisio, 1995 PLEXUS Planning daily tasks Alterman Adapts the experience of riding the SF metro to reuse in NY PRODIGY Planning and learning Veloso, Carbonell Demonstrated in a variety of domains PROTOS Heuristic classification for diagnosis Bareiss, Porter, Murray, Weir, Holte Automatic knowledge acquisition; good for weak theory domains SQUAD Software quality control advisor Kitano 20,000 cases in 1993 SWALE Generates 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

26. name task author obs. CATO Tutoring system Aleven/Ashley Teaching law students to create argument HVAC system Tests and diagnosis of faults in A/C systems Watson, 2000 Diagnosis 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 2001 Planning ongoing care for AD (Alzheimer) cases based on strategies that worked better in past cases HICAP Case-based planning Munoz Avila 1999 Combines case-based planning with methods in planning NEO’s PRUDENTIA Jurisprudence research; textual CBR Weber, 1998 Case retrieval FormTool CBR in color matching Cheetham GE CRD Savings of 2.25 million per year in productivity and cost reduction DUBLET Recommends 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 user’s 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

27. current issues • case authoring • case base maintenance • methods for distributed case bases

28. Building (shells), using, maintaining • Shells/tools • http://www.cbr-web.org/CBR-Web/?info=tools&menu=pt • Esteem examples, NISTP CBR Shell examples Using • Laypeople, experts • Maintaining • Automatically learning new cases • Cases are real or created • Manually adding new cases

29. 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. 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. User’s 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. 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. 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.

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