Case study of Several Case Based Reasoners Sandesh

Overview ➲ Case study of 6 case based reasoners 1 CHEF as a case based planner 2 JULIA as a case based designer 3 CASEY as a case based diagnosis program 4 HYPO as a case based interpretive program 5 PROTOS as a case based classification program 6 CLAVIER as a case based program that is in used and saving the company money.

CHEF -case based planner ➲ Input: sub goal that it need to plan Examples- include fish, use stir frying method... ➲ Output- plan (plan to achieve those goals) ➲ Domain – recipe creation

CHEF ➲ Working creates the plan by recalling old plans with similar circumstances and modify them to fit for new situation 1 retrieve the old recipe that fulfills as many of new goals 2 adapt the old plans to fit the new plans Reinstantiates the old plan by substituting new objet for one used previously Apply special purpose object critics to modify old plan for new situation

For feedback CHEF runs the plan in simulator and provide feedback In failure situation: it tries to explain why it happen Uses 3 repair strategies Splitting and re-forming step in plan Altering the plan to get rid of side effect Finding an alternative plan These failure situations are organized in TOP It update to anticipate similar mistakes using indexes with failure warning

Anticipation of failure step Looks specifically for the plans similar to its current one that failed Add description to current situation to avoid such failures  Knowledge: case library, TOP, object critics, semantic memory  Benefits:  Reuse of plans, repairs plans and learn from experience,

JULIA case based designer Works in the domain of meal planing Problems are decomposed into components and solved separately JULIA uses case based reasoner to propose solution and warn potential failure And constraint posting to keep the track of relationship between the components These constraints describes the relationship between components eg:major ingredients of meal should not be repeated, nutrient balance etc. Constraints are describes in new problems eg: sugerfree,no salt etc

JULIA uses both general and specific requirement impose by clients It’s knowledge is object prototype that stores the different kinds of meals it knows about In problem solving session First tries to fill missing information required for meal planing Find the closest match according to specification If found construct the new solution framework if not choose the appropriate prototype and establish as framework Reconciles the requirement of new situation with established framework Uses case based reasoner to fill in

It uses CBR to construct solution: these steps includes Recalling appropriate cases that fulfill a problem well Adapting retrieved cases In case of conflict, it attempts to adapt the solution to reconcile the conflicts Adapt by minimally adjusting the constraints Interrupts in JULIA With new demand of client Interrupt form retrieval if it retrieves the case that warn of potential for failure Adaptation process Substitutions on ongoing solution Or change the structure of ongoing solution

Structure Goal scheduler- agenda for goal Case retriever Adaptation engine Constraint poster Reason maintenance system Structure maintenance system- ensure solution is internal consistence and with goal

CASEY - diagnosis program Input: description of new patient like signs and symptoms Output : explanation of disorder the patient has Knowledge : library of cases (25approx diagnoses by heart failure program) Domain independent For new patient CASEY searches its case lib fro similar case If not found it pass the case to Heart failure program, HFP diagnoses it and return result to CASEY for future use. CASEY uses 2 step process for case based diagnosis It searches for the similar cases and use model based evidence rules to determine partial matching case Applies model based repair rules to adapt old diagnosis to fit new situation CASEY has different evidence rule for different situations Old case that matches nothing with new case New case that matches nothing with new case Some difference in values

CASEY’s Evidence If two symptoms are both manifestations of same internal state, then they are considered to match each other extra symptom in new case that is not present in old could be a manifestation of some internal state known to exist in the old case- it reconciled the differences Indexing cases by surface features and internal states increase the retrieval performance Domain independent – can be used in wide range of domain, high accuracy and speed of diagnosis process

HYPO – interpretive program ➲ Works in the domain of law ➲ Input- legal situation ➲ Output- creates the arguments for legal client ➲ Steps ➲ Analyze the case for relevant factor ➲ Retrieve the case that share those factors ➲ Position the retrieved case with respect to new one ➲ Select the most on point case from each set ➲ Argue the case ➲ The analysis is done while arguing the issue is used to explain and justify the arguer’s point ➲ Hypothetical case is created and used to test

PROTOS a classification program Case base classification and case based knowledge acquisition Domain – audiological (hearing) disorders Searches the cases in two steps First it narrow search by finding most likely candidate Based upon the quality of best match candidate and new item, it follows pointer around case library in search matches. It is repeated until it find the best solution or fails It implements generate test debug First guess the category of new problem overlapping the features in old cases If the match is not successful, it uses the result to find better hypothesis. In case of strong misclassification, PROTOS uses a link form wrong to correct category and avoid making same mistake again in future Uses surface features for matching

PROTOS has 4 connections between its cases and categories Reminding links- allows best guess in category to choose Prototype links- connects category to item that represent most Difference links-import differences between items Censor links- rule out connections that might otherwise be made In case of failure expert provides PROTOS with appropriate knowledge, it also add difference link to its memory to avoid same mistake in future

CLAVIER System for configuring the layout of composite airplane parts for curing in an autoclave Input: set of parts tat need curing Output: layout for several loads of the autoclave

Adaptation mechanism In situation if retrieves does not fulfill the case exactly CLAVIER uses experience to guide adaptation Ie. For each peace that need to substitute, it looks for table from previous load that has the pieces on current table+some piece from the input set that still needs curing Then chooses the part that table suggests For this it uses global knowledge providing the context in which the table is located and local knowledge which describes the table itself Advantages: Ability to learn Disadvantages: Validating new case and maintaining case library