Case-based reasoning

Artificial Intelligence

In case-based reasoning, the cognitive agent addresses new problems by tweaking solutions to existing problems.

Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning.

The problem we are trying to solve here is what happens when you are confronted with a new situation and you don't have a good past-case to solve the new problem.

The basic process

Process	Description
Retrieval	Retrieve a case from memory similar to the current problem
Adaptation	Adapt the solution to that case to fit the current problem
Evaluation	Evaluating how well the adapted solution addresses the current problem
Storage	Storing the problem and solution as a case

Assumptions

Assumptions of case-based reasoning are:

1. there are patterns to problems in the world (the same type of problem repeats again and again)
2. similar problems have similar solutions (not always but mostly)

Adaptation

When we solve a new problem, we are remembering a problem and slightly modifying (adapting it) to solve our new problem.

Using models for case-based reasoning

We can use models (or previous patterns) of a problem to solve a new problem.

Using recursive reasoning

In this approach, I break my problem in to parts. I first find a solution to MOST of my problem. I then go back into memory and look for a solution which most closely matches the other part of my problem. I might do this again and again.

Use case-based reasoning by rules

The key point here is use heuristics. A heuristic is a rule of thumb that works often but not always.

Apply knowledge or rules to put theory into practice.