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

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

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

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Case-based reasoning

ISYS 370R. Weber

Page 2: Copyright R. Weber Case-based reasoning ISYS 370 R. Weber

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CBR applications

CCBRconversational CBR

http://www.egain.com/pages/Level2.asp?SectionID=4&PageID=4

http://support.lucasarts.com/yoda/start.htm

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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.

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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.

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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.

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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.

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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

ARCHIEARCHIE 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 problemHYPO Interpretation and

argumentationRissland & 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    

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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    

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name task author obs. CYRUS

stored and retrieved events in the life of Cyrus Vance when he was secretary of state

Kolodner first implementation of MOPs reconstructive dynamic memory

AQUA

story understanding, explanation on terrorism

Ram reads newspaper stories and asks questions, learning through incremental revision of knowledge; case-based explanation

CASCADE assistance on recovering from crashes in VMS OS

Simoudis & Miller

(first) help desks; emphasis on efficient retrieval when first descriptions are not rich

ASK user directed exploration of stories and guidelines describing a task or domain

Ferguson, Bareiss, Schank

ASK Tom trust bank consulting; ASK Michael industrial

CELIA automated diagnosis and interactive learning; predicts an expert’s action and relate steps

Redmond acquiring cases, learning indexes, combines cbr and other methods

Mostly from Kolodner 1993

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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 maintenanceOperated by salespersons Western AustraliaThe Auguste

ProjectCBR 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 phonesEmploys 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 filteringCF 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

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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.

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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)

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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).

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CBR methodologyTask?

AI Task:

DiagnosisPrescriptionInterpretation-adviceRecommendationAnalysis-predictionSchedulePlanning

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casebase

caserepresentation

CBR methodologyTask?

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CBR methodology

casebase

situationassessment

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CBR methodology

casebase

RETRIEVE

REU

SE

REVISE

RETA

IN

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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

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Case representation

• case problem: symptoms A, B, C• case solution: disease 1• case outcome: confirmed

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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

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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

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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

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Adaptation

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

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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

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Learning

• learning by incorporating new cases to the case base

• learning by adding cases that are adaptations from retrieved cases

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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

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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 recommendati

on

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vocational counseling

diagnosing headaches

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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.

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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.

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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.

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current issues

• case authoring• case base maintenance• methods for distributed case bases

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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

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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.