What Is Case Based Reasoning

Case-based reasoning (CBR) uses past experiences (cases) to solve current problems. The concept of reasoning from relevant past cases is intriguing because it corresponds to the process an expert uses to solve "new" problems quickly and accurately./p>

The ability to retrieve and manipulate past problem-solving examples accurately is important for types of problems in the following areas:

• Diagnosis
• Predicting
• Planning
• Classifying
• Designing
• Law
• Process Control
• Monitoring
• Advance Manufacturing

There are advantages of remembering past cases for problem solving, especially with computers. Humans tend to forget what or why something has worked in the past. This leads to the reinventing-the-wheel syndrome.

Computers do not forget! If we get computers to remember the right thing at the right time, we can reap the benefits of past successes and failures.

This is the ultimate goal of Case-Based Reasoning

Case-based systems search their case memory for an existing case that matches the input specification. As new cases are solved they are added and continue to increase the database of cases solved. Thus you continue to increase your likelihood of success.

The goal is to find a case that matches the input problem and goes directly to the solution, making it possible to provide solutions to potentially complex problems quickly. If, on the other hand, we are not able to find an exact match, the system will find one similar to our input situation to provide it as a completed solution. Since the system can learn, when a nonperfect match is found but the problem is solved, the case is added to the systems case memory for future use.

Learning is a key part of a CBR system’s architecture.

To understand how CBR works, one must understand what a case is to a computer. In the simplest form, a case is a list of features that lead to a particular outcome. Some examples on a credit application would be: credit cards, amount of loans outstanding, value of assets, etc. In it's most complex form, a case is a connected set of subcases that form the problem-solving task's structure -- for example, the computer chip on a computer board. The designs of the computer chip and the computer are made up of subdesigns of the components that comprise the whole, each of which could be considered a case unto itself.

One of the key differences between rule-based and case-based knowledge engineering is that automatic case-indexing techniques drastically reduce the need to extract and structure specific rule-like knowledge from the expert -– the most time-consuming part of rule-based knowledge engineering.

CBR systems derive their power from their ability to retrieve relevant cases quickly and accurately from its memory. Figuring out when a case should be selected for retrieval in similar future situations is the goal of case-indexing processes.

Building a structure or process that will return the most appropriate case is the goal of the case memory and retrieval process. Case indexing processes usually fall into one of three kinds: nearest neighbor, inductive, or knowledge-guided, or a combination of the three.

One of the advantages of the fully integrated Mindsuite is the use of the Mindsuite Data Mining Technology to improve the case indexing.

The Mindsuite CBR technology is self-adaptive; it offers the most similar relevant case to the input situation. Case adaptation takes a retrieved case that meets most of the needs of the current situation and turns it into one that meets all of the situation's needs. Case adaptation also involves making minimal changes to the input requirements to meet a known goal embodied in a stored case.

Learning and generalization are critical to CBR systems.

Taking advantage of existing techniques for extracting useful information from examples lets case-based systems avoid some of the main problems of rule-based approaches in gathering problem-solving or classification knowledge and putting it to good use.

As cases accumulate, case generalization can be used to define prototypical cases that can be stored with the specific cases, improving the accuracy of the system in the long run.

The inductive-indexing capabilities in CBR systems provide several major advantages over neural networks and pattern-recognition techniques. Inductive systems can represent and learn from a wider range of feature types than either neural networks or pattern recognition. The ability to use richer feature sets for describing examples makes them at least as accurate and many time more precise.