CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling1 Chapter 7: Methods for Index Selection The indexes of a case allow us to retrieve it when we need it again A useful index has four properties It should be predictive It should make useful predictions It should be easy to recognize It should be generally applicable Indexes are typically specialized for the problem domain Attempts to automate or generalize index selection only work in the simplest of problem domains

CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling2 Index Selection: CHEF Example An index may be stored as part of a case or may be external to the case Consider CHEF A case contains a problem and a solution The problem is the goals you want to achieve, like making a stir-fry dish that contains beef and broccoli The solution includes the ingredients for the dish and the steps you need to take to prepare the dish Some indexes, like contains-beef and contains-broccoli, are parts of the case Other predictive indexes are not part of the case itself Contains-meat is more general than contains-beef. It allows us to predict that a vegetarian won’t like the dish Avoids-soggy-broccoli-problem allows us to make predictions about other recipes in which vegetables could get soggy

CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling3 Steps in Index Selection To choose good indexes for a case: Determine what tasks the case is useful for Determine when the case is likely to be useful Choose features of the case that predict the case will be useful for those tasks under those circumstances Make those features more general, if possible Describe those features using vocabulary your computer program can recognize and process

CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling4 Index Selection: JULIA Example Problem: 20 people were coming to dinner It was summer Tomatoes were in season The meal should be vegetarian One guest was allergic to milk Solution: Serve tomato tart (a cheese and tomato pie) Adapt one tart by using tofu cheese substitute instead of cheese

CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling5 Index Selection: JULIA Example (continued) This case is useful for: Finding a vegetarian main dish with tomatoes Adapting a dish with cheese when you have to eliminate milk products The tasks this case can help with are: Choosing a main dish Adapting a dish to eliminate cheese The circumstances under which this case will be useful are: When you need to choose a vegetarian main dish in summer When you need to eliminate cheese from a main dish Note that you need to think about the case from all angles, even if it seems like you’re just saying the same thing in different ways

CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling6 Indexes in JULIA

CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling7 Indexes in JULIA (continued)

CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling8 Checklist-Based Indexes Some systems are able to index all cases by the same features The analysis is done up front in figuring out which features are predictive When it works, this facilitates later reasoning, because the system can always look in the same slots to find predictive features CASEY could use a checklist approach, because the patient’s symptoms were always what was needed to predict that one patient’s diagnosis would be useful for another patient CHEF could get some, but not all, of its indexes this way Cases were always indexed on major ingredients and preparation method Other indexes were case specific

CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling9 Difference-Based Indexing You can sometimes reduce the number of indexes by considering how well different features differentiate cases from each other For example, if you know a patient’s temperature is predictive, but most patients have a normal temperature, you could use temperature as an index only when a patient has a fever Depending on your retrieval approach, having fewer indexes may improve efficiency Note: In a diagnostic system, differentiating features and predictive features are exactly the same thing. In our system to prescribe neuroleptics for Alzheimer’s Disease patients, most of the effort required was spent determining which features distinguished patients who took neuroleptics from those who did not Patient charts contained hundreds of features. A physician recommended the top 100 features, and we narrowed this down to ten predictive indexes

CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling10 Explanation-Based Indexing If a system is able to generate explanations of why a solution did or did not work, these explanations make useful indexes This is what CHEF used to get indexes like avoids-soggy-vegetable- problem This isn’t possible in domains that are not well understood For example, Lockheed engineers never knew why one way of loading the autoclave worked and another didn’t When a system (or a person) can create good explanations, then this is a good way to index cases