Case-based reasoning. INFO 629 R. Weber. 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.
CBR applicationsCCBRconversational CBRhttp://www.egain.com/pages/Level2.asp?SectionID=4&PageID=4http://support.lucasarts.com/yoda/start.htm
Romantic advisor; retrieves a similar history
Predict power demand
Same result but faster than human experts
Architecture design of office buildings
Design of mechanical components
Abstract indexing allowed innovative design
Diagnosis cause and prescribes solution to heart problems
Diagnosis and repair; customer support help desks
Uses Inference’s tool; can be used by up to 60 users at a time; shows that library engineering is necessary
Design of recipes to meet different simultaneous goals
case-based planning: Memory started with 20 recipes and learned from user feedback
Design and evaluation of autoclave loading
Barletta & Hennessy
Interacts planning and scheduling
Planning soccer games
Debugging and fixing bad strategies; memory keeps strategies and the type of problem
Interpretation and argumentation
Rissland & Ashley
Retrieves similar cases to create a point, a response, and a rebuttal using hypotheticals (Ashley, 1990)
Defines sentences of delinquent crimes based on the chances of repeating the crime and its severity
In case of not having a sufficient similar case, the system uses heuristics to determine the sentence
Plausible reasoning and design
Mediates conflicts by performing planning
Keeps in memory failed solutions and tries to avoid same failures in new solutions
Mediation of union negotiations; proposes solutions with arguments
Considers part’s goals and considers recent accepted solutions
suggests how to write papers
Planning daily tasks
Adapts the experience of riding the SF metro to reuse in NY
Planning and learning
Demonstrated in a variety of domains
Heuristic classification for diagnosis
Bareiss, Porter, Murray, Weir, Holte
Automatic knowledge acquisition; good for weak theory domains
Software quality control advisor
20,000 cases in 1993
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
Teaching law students to create argument
Tests and diagnosis of faults in A/C systems
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
Planning ongoing care for AD (Alzheimer) cases based on strategies that worked better in past cases
Munoz Avila 1999
Combines case-based planning with methods in planning NEO’s
Jurisprudence research; textual CBR
CBR in color matching
GE CRD Savings of 2.25 million per year in productivity and cost reduction
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.
Springer series on CBR Research and Development
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.
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.
(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.