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Chapter 2: Case Studies of Several Case-Based Reasoners

• • • This chapter describes some classic CBR systems that illustrate different ways in which CBR can be used • • One of the best known CBR systems is the planning system CHEF CHEF generates plans for cooking, that is, recipes It was built by Chris Hammond, in the AI Lab, at Yale We will use CHEF to illustrate the flow of reasoning in CBR CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 1

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CHEF

• • • • • In a CBR system, the input describes the problem you want to solve or the situation you want to interpret • Sample Input for CHEF: Request for stir-fry beef and broccoli recipe Next we need to assign indexes Indexes are features of the new problem that characterize it and that are relevant to finding similar problems in the case base • • • Indexes for CHEF example: Stir-fry-dish Contains-beef Contains-broccoli CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 3

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CHEF, continued

• • • • The input and indexes are used to retrieve a similar past case The past case contains the prior solution Matching rules are used to find the most similar, or most useful, case CHEF wants a recipe for stir fry beef and broccoli. It retrieves: Recipe for Stir-Fry Beef and Green Beans CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 5

CHEF, continued

• • • The next step is to adapt the old solution so that it will meet all of the goals of the present problem CHEF has a rule that tells it one vegetable can be substituted for another in a recipe It also has rules to account for differences in ingredients. For example, broccoli needs to be chopped and cooks for less time than green beans Recipe for Stir-Fry Beef and Green Beans Cook time X Recipe for Stir-Fry Beef and Broccoli Cook time Y Step: Chop broccoli CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 6

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CHEF, continued

• • • • • The next step is to test out the new plan or solution to see if it is satisfactory CHEF uses a simulator that checks a recipe against known criteria for good stir-fry cooking • CHEF’s evaluation for this example: Failure: The broccoli is soggy • When a failure is detected, it needs to be explained, so that the system will learn from it and not make the same mistake again CHEF’s simulator knows that the broccoli is soggy because it sat too long in the meat juices Explanation: meat juices to blame CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 8

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CHEF, continued

• • • • The causal analysis guides the repair of the plan Repair strategies are stored and indexed based on the types of problems known to occur CHEF’s strategy for soggy broccoli is called SPLIT-AND-REFORM It repairs the recipe by adding in steps to stir fry the broccoli separately and then mix it in Recipe for Stir-Fry Beef and Broccoli Cook time Y Step: Chop broccoli Step: Stir-fry broccoli in separate pan Step: Mix broccoli in with other ingredients CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 10

CHEF, continued

• • • • • Now CHEF has a good recipe to store in the case base • • • • To avoid the soggy vegetable problem in the future, CHEF indexes this recipe by Stir-fry-recipe Contains-beef Contains-broccoli Avoids soggy vegetable problem CHEF improves its performance by checking for this problem in the future A CBR system learns by acquiring new cases A CBR system learns by anticipating and avoiding failure CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 11

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CASEY

• • • • CASEY diagnoses cardiac disease based on the diagnoses of earlier patients It was built by Phyllis Koton, at MIT • • The features, or indexes, for a patient are measures and states Measures are clinical data, like heart rate States can be qualitative measurements, like high left arterial pressure, or the presence of a disease, like pericarditis, or therapies given, like nitroglycerin To support a diagnosis, CASEY builds a causal explanation, in the form of a graph, showing links between measures and states to indicate causality CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 13

CASEY, continued

• • • • CASEY compares two cases by determining what the differences are and whether or not they are significant • An old case may have a feature that the new case does not have If it was not used in the diagnosis, then it is irrelevant whether or not the new case has it • If the feature was used in the diagnosis, then CASEY looks to see if the new case has another feature that would cause the same states as the missing feature. If so, the match is still good.

If all differences are irrelevant or can be “fixed up,” the cases are considered to be similar, and the diagnosis is made The diagnosis is adapted to to the new patient by substituting the new causal relations and the new patient’s data CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 14

CASEY, continued

• • • • • • CASEY was built on top of the Heart Failure Program, a model based system that was considered to be accurate, but slow Whenver CASEY could not find a similar enough case in its case base to make a diagnosis, it invoked the Heart Failure Program CASEY obtained initial cases for its case base by running this system CASEY also took knowledge about which features were and were not significant from this system CASEY’s diagnoses were found to match those of the Heart Failure Program most of the time, but it operated one to two orders of magnitude faster Efficiency was CASEY’s original claim to fame, but it has since been recognized for integrating reasoning modes and for illustrating CBR’s usefulness in medical domains CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 15

JULIA

• • • • • JULIA is a CBR design system It designs menus for dinner parties, serving in the role of a virtual caterer It was built by Tom Hinrichs, at Georgia Tech The inputs to JULIA are the constraints that any satisfactory menu must meet Example: (host tom) (guests jlks-research-group) (cost cheap-meal) (ease-of-prep easy) (ingredients (tomato cheese)) CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 16

JULIA, continued

• • • • • • JULIA checks its case base for matching menus and interactively asks questions to try to narrow down to a good menu In our example, JULIA asks “Would you like lasagne, garlic bread and red wine?

The user may agree, but if the user disagrees, JULIA will look for more menu choices in the case base The user may also add new constraints at any time, much as they would in interacting with a human caterer JULIA uses both cases (specific dinner party menus) and prototypes (outlines of menus) It also integrates CBR with constraint problem solving, another AI approach applicable to the menu planning process CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 17

HYPO

• • • • • • HYPO is an example of interpretive CBR, rather than problem solving CBR It was built by Kevin Ashley, at U Mass Amherst HYPO’s task is argumentation for legal reasoning in the domain of trade secrets law Trade secrets law cases typically involve employees of one company giving or selling proprietary information to another company, giving that company a competitive advantage • Trade secrets law cases are decided in courts of law based on precedents found in previous cases HYPO works by inputting a current case and finding “most-on-point” cases to it from a case base of around 30 real trade secrets cases Cases are most-on-point if they share the most features in common CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 18

HYPO, continued

• • • • • The features used in HYPO were those determined by legal scholars to be important in deciding trade secrets cases • • • • Example features: Had the accused employee signed a nondisclosure agreement Had the accused company bribed employees to switch employment or to give them secrets Had the plaintiff company already disclosed the secrets to others Had the accused employee given the plaintiff’s notes, diagrams, or tools to the accused company HYPO retrieves cases that have both desired and undesired outcomes It suggests hypothetical ways of strengthening a case by locating additional information that would be helpful in getting a positive ruling Lawyers then argue their cases based on the precedents HYPO provides CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 19

PROTOS

• • • • • • • PROTOS is a CBR classifier It was built by Ray Bareiss It uses classification to diagnose an audiological patient as having one of a number of known audiological disorders PROTOS contains approximately 200 cases and 15 different disorders It was built by having an expert audiologist classify real audiological patients To classify a new case, PROTOS uses reminding links to suggest a potential classification • • It compares the new case to the cases in that classification first If it finds a close match, it extends that classification to the new case If not, it uses difference links and censor links to suggest a new classification to try CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 20

PROTOS, continued

• • • • There are other AI approaches to classification that are easier to use when they are applicable These include neural networks and decision trees • Automated classification techniques did not work well in PROTOS’s domain because of the nature of the data There were 58 different features a patient could have, but on average, a given patient only had values recorded for 11 of these Neural networks and decision tree techniques do not deal well with this much missing data CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 21

CLAVIER

• • • • • • CLAVIER is a design system that was fielded at Lockheed by D. Hennessy and D. Hinkle It was the first CBR system to be used on a factory shop floor (1990) CLAVIER determines how to best load an autoclave, a large, pressurized, convection oven Lockheed manufactures parts for aerospace applications • One critical step is to cure these parts in an autoclave Lockheed engineers do not fully understand the properties of the autoclave and believe them to be too complex to define using equations or rules • Before CLAVIER, shop floor operators worked from sample oven configurations They tried to fit the parts that were ordered into configurations known to produce good results CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 22

CLAVIER’s Oven Configurations

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CLAVIER, continued

• • • CLAVIER began by using 20 of these manual configurations as cases • • It input a prioritized list of ordered parts and looked for the best matching configuration If multiple configurations could be used, the best match was the configuration that used the highest number of high priority parts If no configuration could be completely filled out, the best match was the one with the fewest missing parts • • Initially, Hennessy and Hinkle tried to adapt incomplete configurations by including parts similar to the missing ones This worked OK from a research perspective, but every mistake was very expensive Lockheed engineers decided to just cure the missing (unordered) parts and save them against future orders CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 24

CLAVIER, continued

• • • • CLAVIER became more successful as it acquired cases over time Each new successful configuration was added to the case base until there were 150 cases • At this point, they could find an exact match 90% of the time It grew close to becoming a table lookup system • • • Advantages of CLAVIER were: It used CBR outside the research lab It captured load configuration expertise, which could be used by non-expert employees It provided working solutions even though engineers didn’t understand the problem well enough to derive solutions from first principles CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling 25

Other CBR Systems

• • Not all CBR systems implement all phases of the CBR flow chart or work in exactly the same way • • • One important twist is that CBR is useful for advisory and/or educational systems These systems are typically less automated than the ones we’ve discussed so far Often, just retrieving past examples for a user can help a person to identify and/or solve a problem on their own A typical application is the CBR help desk, which works by storing cases of reported problems that the help desk operators have already solved. Since different users report the same problems over and over, good solutions often already exist that can be recalled.

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