A survey of common techniques used for developing CBR systems

Ahmad Atta

Knowledge Representation

1. Case Representation

2. Case base representation

General definition [Kolodner, 1993] Problem Description

GoalsConstraints on the goalsProblem situation

SolutionSolutionsReasoning steps

OutcomeOutcomeExpected failureRepair strategy

Case(behavior) in DarmokThe declarative part

A goalPreconditionsAlive conditions

The procedural partBasic actionssub goal

The state of behaviorPending , executing, succeeded , or failed.

The state of goalOpen , Ready , Waiting.

Case presentation in CBR-BDI AgentsP = < E, o , o’ , R , R’> where :

E is the environment (e.g. game state)O the objectives of agent.O’ the results achieved by the planR the total resources !!R’ the resources consumed by the agent

Case base representationCase base memory in Darmok consists of two

elements: 1. Snippet

The procedural part of the behavior.

2. Episodee = ( P , G , S , O) where e.P is the snippet, e.G is the

goal, e.S is the situation , and e.O is the outcome of applying e.P in e.S to achieve e,G.

Case memory structure 1. Episodic Memory Organization packets

2. The Category & Exemplar Model

Case RetrievalCase Retrieval in Darmok

1. Episode relevance measure ER ( e, S, G)2. ER( e, S, G) = GS(e.G, G) + (1-0) SS(e.S , S)3. Retrieve the episodes with maximum

relevance RE(p, S, G) = {e1, ……, ek}

4. Define the predicted performance for each snippets

AdaptationPlan Dependency Graph GenerationPrecondition-success condition matcher (ps-

matcher).Removal of unnecessary actionsAdaptation for unsatisfied preconditions