Memory in problem solving

Let us finish this topic by seeing how memory is employed in problem solving.

We have seen that many problems are solved by analogy. Computer systems that perform this task are sometimes called case based reasoning (CBR) systems.

CBR systems employ large case libraries rather than descriptions from first principles.

They therefore rely heavily on memory organisation and retrieval.

• A rich indexing system must be employed -- when reasoning with a problem only relevant past experience should be recalled.
  • Index by features present in problem.
• Require some measure of relevance of retrieved information.
  • Some features only important in a certain context.
  • Inductive and explanation based learning suitable here.
• The data structures used will be important as the number of cases represented will be large.
  • Do we retrieve all information about a case or fragment of it?

• A number of cases are usually retrieved. We need to select the best one using some heuristic which may include:
  • Goal directed preference -- cases that include same goal as current problem.
  • Salient feature preference -- cases that include the most important (or largest number of) features.
  • Specificity preference -- certain match features identified.
  • Frequency preference -- select frequently matched cases.
  • Recency preference -- select recently matched cases.
  • Ease of adaptation preference -- cases whose features easily modified for new problem.