1. NTUST Ailab Li-we Pan The MEDIATOR : Analysis of an Early Case-Based Problem Solver Jenet L. Kolodner & Robert L. Simpson Cognitive Science 13, 507-549 (1989) 指導老師 ： 何正信教授 學生：潘立偉 學號： M8702048 日期： 88/01/27

2. NTUST Ailab Li-we Pan Context Introduction An annotated example The MEDIATOR as a case-based reasoner Discussion Concluding 1

3. NTUST Ailab Li-we Pan Introduction Capitalizes on previous problem-solving experiences when solving new problem. CBR can improve problem-solving performance in : –providing reasoning shortcuts –warning of the potential for error –suggesting a focus for reasoning –directing the problem solver through pathways 2

4. NTUST Ailab Li-we Pan The MEDIATOR Task domain : common-sense advice giving for resolution of resource disputes Derive a compromise agreement that will resolve a dispute between two disputing parties the task that want to do : –understand the needs of the parties in order to find areas of potential agreement –suggests a solution that is either accepted or rejected by the disputing parties. –Based on the feedback given by parties when they reject a solution, the mediator derives a new solution plan or attempts to persuade the rejecting party to accept the settlement. –Until both disputing parties are satisfied with suggested. 3

5. NTUST Ailab Li-we Pan I. Understand Problem.Interpret Problem.Classify Problem.Elaborate Problem Elaborate Disputant Goal II. Generate Plan.Choose Planning Policies.Select Abstract Plan Refine abstract plan Choose plan actions Bind plan variables.Predict Results III. Evaluate Results (interpret feedback) IV. Recover from Failure.Understand failure.Remedy reasoning error.Solve new problem The MEDIATOR’s reasoning subgoals 4

6. NTUST Ailab Li-we Pan The MEDIATOR as a CBR Use one of the method: –value transfer : a process of extracting a previously used value to achieve some subgoal and propose it for transfer to the new case. –Partial instantiation : a process if transferring the template(frame) of the solution from the previous case and filling in as many of the details(slots) as possible. the solution is composed of the frame type and the slot fillers. 11

7. NTUST Ailab Li-we Pan Used CBR for three task –To choose frames for representation(e.g., during problem classification, abstract plan selection, choice of plan actions, failure explanation, and fail remedy) –to choose values to fill slot(e.g., when choosing planning policies); –to instantiated frame variables(e.g., when binding plan variables). Use similar Dynamic memory 12

8. NTUST Ailab Li-we Pan Choose a plan –a meta-planning phase : establishes an overall planning policy that guides later planning decisions. –a plan-selection phase : beginning at the highest level of abstraction, chooses the most promising general plan believed applicable for the problem. –a plan-instantiation phase : specifies the plan roles and actions. –a prediction phase : generates a specific set of expectations based on the assumption that all actions are executed as planned. 13

9. NTUST Ailab Li-we Pan Explaining failures –misunderstanding failures –planning failures –plan execution failures –evaluation failures –unsolvable problem failures use “from-scratch” way 14

10. NTUST Ailab Li-we Pan Discussion Usefulness of Case-Based Reasoning –broad applicability of CBR –reasoning shortcuts on many-step inference chains –behavior in one-step inference –anticipating and avoiding failures\ –caveats 15

11. NTUST Ailab Li-we Pan Making a Case-Based Inference control and facilitation of case-based inference –Locating and choosing a candidate case –Deciding which part of a case to focus on –deriving subgoals –integrating case-based and goal-directed reasoning. Case-based reasoning and analogy 16