CaseBased Reasoning

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Case-Based Reasoning

• JL Kolodner (1993), Chapters 1-3 of Case-Based
  Reasoning, Morgan Kaufmann Publishers, San Mateo,
  CA
• presented by
• Akash Shah

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What is CBR?

• Old solutions -gt New problems
• Old cases -gt Explain new situations
• Old cases -gt Critique solutions
• Reason from precedents -gt Interpret new situation
• Equitable solution -gt New problem

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Reasoning depends on 5 things

• Experiences
• Understand new situations in terms of old
  experiences
• Adapt
• Evaluate and repair
• Store

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Experiences

• More experiences gt Less ?
• Creative in understanding adaptation
• At least some relevant experience
• Should start with a representative set
• Goals and subgoals
• Successful and failed attempts

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Understand a problem

• Recalling old experiences
• Indexing problem
• Interpretation
• New problem compared with old ones
• Not needed much when a problem is well understood

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Adapt

• Old solution
• Add
• Delete
• Substitute

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Evaluation Repair

• Right solution/Wrong solution
• Feedback

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Storing

• Storing cases helps in becoming
• More efficient
• Steps required to solve new problem need not be
  repeated
• More competent
• Better answers over time
• Anticipate mistakes by prediction

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Case

• Case is a contextualized piece of knowledge
  representing an experience that teaches a lesson
  fundamental to achieving the goals of the
  reasoner
• Knowledge tied to a context, not general
• When to store a case?
• Difference causes a difference
• Teaches a lesson for the future (useful lesson)

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Case based reasoning cycle
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Advantages

• Quick
• Domains not completely
  understood
• No algorithmic method
  available

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Advantages (cont.)

• Open ended/ill defined concepts
• Warning
• Focus reasoning on important parts of the problem

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Disadvantages

• Use old cases blindly
• Bias towards old cases for new problems
• New user may not reminded of most appropriate set
• Does not fully explore the solution space. So
  most optimal solution may not be found

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CBR People

• Planners, economists, stock-market analysts
• We can use CBR to aid people
• Retrieval tool to augment memories
• Teaching based on examples
• People can learn CBR to allow them to not blindly
  trust past cases. Justification. Evaluation.

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Building CBRs

• Types of CBRs

Fully automated
Retrieval only
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Reasoning using cases

• What kind of tasks does CBR support?
• Tasks can be categorized as
• Problem solving design, planning, diagnoses
• Interpretive understanding, justification

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Planning

• Sequence of steps to achieve some state in the
  world
• A plan must have a sequence of steps. A later
  step cannot undo the work of an earlier one
• CBR helps in providing plans that have already
  worked, minor fixes required
• Previous plans are saved and indexed by
  conjunction of goals they achieved so we may be
  able to solve achieve several goals at the same
  time
• Planning vs execution
• Put off some planning until execution
• CHEF, PLEXUS, TRUCKER, MEDIC

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CHEF case based planner
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CHEF (cont.)

• Beef and broccoli
• Include beef Broccoli Stir - fry
• Finds beef green beans
• Include beef stir fry
• Partial Include vegetables (adaptation)
• Suggests recipe with
• Tender beef, Crisp broccoli, etc.

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CHEF (cont.)

• FAIL!!! Broccoli was soggy
• Tries to understand what went wrong
• Repairs it by using repair strategy

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CHEF (cont.)

• Adds the lesson learnt to library
• Uses it next time to make chicken snow peas
  recipe

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CHEF (cont.)

• Powerful case library
• Strategies to repair failures (SPLIT-REFORM)
• Semantic memory (crisp, vegetable)

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Design

• Problems defined as set of constrains
• Remember old design case that was created with
  constrains similar to the current ones
• Starting from scratch can be very tedious as
  constrains may be violated due to interactions
  between parts
• Cases provide a glue to hold the solution
  together
• JULIA, CYCLOPS, KRITIK, CADET, ARCHIE, CLAVIER
• Mediators too fall under design category as over
  constrained problems MEDIATOR, PURSUADER

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JULIA (case based designer)

• User given constraints (general and specific)
  food should be tasty, max number of calories,
  vegetarians coming
• Cases object prototypes (American dinner 3
  courses salad main course dessert)
• Focuses on some aspect of solution by recalling
  cases that fulfill particular constraint
• Adapt, put in ongoing solution, resolve conflicts
  by adapt, then by relaxing some constraint, then
  by backtracking

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JULIA (cont)

• Interrupts
• Client Akash, a vegetarian, is joining us for
  dinner! (JULIA begins to panic ? )
• Tries to repair to accommodate, treats this just
  like a conflict
• Own retriever Potential for failure
• For example, a case might warn that a vegetarian
  may come for dinner (she learnt from her previous
  mistake). JULIA will then pause, maybe ask the
  user if that is the case, if yes, then above
  situation.

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CLAVIER

• A number of parts need to be cured in an
  autoclave at Lockheed
• CLAVIER will take the list of parts as input and
  design layouts for several loads of the autoclave
  that will cure all the parts, getting as many of
  them cured at a time as possible
• Started with 20 cases, collection over a 100 more

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CLAVIER (cont)

• Looks if any previous load had current table
  some piece from input that still needs curing

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CLAVIER (cont.)

• Context determination and context matching
• Global knowledge material used, grouping
• Local context where is the table in the oven,
  size of the parts it holds
• Allows less experienced autoclave operators to
  load an autoclave like an expert

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Explanation Diagnosis

• Credit/Blame assignment problem
• Remember similar phenomenon, borrow its
  explanation and adapt
• Diagnosis is an explanation problem where
  symptoms are given and system is told to explain
  them
• When small number of explanations, this becomes a
  classification problem
• CASEY, PROTOS

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CASEY (case based diagnostics)

• CASEY is built on top of a model-based diagnostic
  program called Heart Failure Program that
  diagnoses heart failure
• If similar case exists, then make a diagnosis,
  else pass it to Heart Failure Program.
• Return result to CASEY

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CASEY (cont.)

• For new patient, try and match old patient
• Try and reconcile the differences
• Indexes cases based on both surface features
  (blood pressure, age, etc) and internal states
  (tries to see if some symptom is as a result of
  something else and thus reconcile difference)
• As accurate as Heart Failure Program, but 2-3x
  times more efficient

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PROTOS (case based classification, case based
knowledge acquisition)

• Description of situation -gt Classify into type
• Misclassify -gt consult expert -gt correct
• 4 kinds of connections between cases and
  categories
• Reminding links features to categories
• Prototype links connect categories to items
• Difference links differences between items
• Censor links rule out connections

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Interpretive CBR

• Evaluate situations in context of previous ones
• Justify arguments proof of rightness of an
  argument - HYPO
• Projection predict effects of a solution i.e.
  provide a way to project results based on what
  has been true in the past
• Common theme in all is argumentation. Some cases
  will support one stand, some the other.

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HYPO (interpretive reasoner)

• Legal situation -gt argument for client
  (defendant/plaintiff)
• Take best point in favor, best point against
• Difference between cases are examined, cases
  addressing those differences in favor are chosen

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Retrieval only systems

• Architects assistant
• Dispute mediators assistant
• Real aiding systems Battle Planner, help desk
  implementations,

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Comparison with other heuristic methods

• Rule based reasoning
• Knowledge extracted from experts and encoded in
  rules.
• Importance on form of knowledge, not content.
• Should match input exactly
• Can give rule chains as explanations whereas CBR
  can only give cases from where it got solution
• Model based reasoning
• Store models of domains
• Applicable when domain is well understood
• Can verify solution, but generation is unguided

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Thats all folks!