Case Based Reasoning (CBR) is a methodology for "learning from experience". Knowledge in a CBR system is composed of past examples, or cases, stored in a database or by some other convenient means, known as a casebase. To extract information, a target example is created, and the database is searched for the closest matches to the target. For example, consider a call center providing computer technical support. When a customer calls in with a computer problem, the agent fills in a form on her computer screen describing whatever information is known. The CRM system then searches a database of previous solved problems to find the closest matches and suggests questions to ask the caller. As more and more information is filled in at each step, the database is repeatedly searched for closer and closer matches until a solution is found. In this way the accumulated expertise of past problems is applied to the problem with very little expertise required of the agent.
In effect, the operative principle of CBR is this: Reasoning by remembering.
The Case Based Reasoning process is usually described as 5 steps: Representation, Retrieval, Reuse, Revision, Retention.
Representation is a key issue for selecting CBR as the technology implementation choice. CBR systems are able to represent perhaps the widest range of data types of all the knowledge representation technologies. While neural networks are limited to numeric data types, and expert systems are limited to numeric and symbolic types, CBR systems can represent virtually any type of data provided. Some typical data types handled by CBR are numeric, symbolic, categorical, heirarchical, and text. Any data type that can provide a method of matching one example to another can be effectively used by a CBR system. Note: some commercial CBR products offer nothing more than text matching.
Retrieval is the process of finding the cases in the casebase that most closely match the current information known. The current information is represented as a new case with much of the information missing. This step of matching cases is perhaps the most crucial step in the CBR methodology.
Reuse is the step where matching cases are compared to the new case to form a suggested solution.
Revision is the testing of the suggested solution to make sure it is suitable and accurate. The result is a solution that has been confirmed by testing.
Retention is the storage of new cases for future reuse. A strong advantage of CBR over other reasoning technologies is the fact that new knowledge is continuously and easily added to the store of experience. This step is usually as simple as storing the new case into the casebase.
Case Based Reasoning is best applied to problems where experts would explain how to solve a problem by giving examples. It is easiest to apply when a database of past cases exists, but can be "bootstrapped" from only a few examples, as well. Application areas where CBR has been shown to excel are:
- Call Centers / Help Desks:This is an excellent area of application for CBR. As second tier agents with advanced expertise add newly solved cases to the database, this expertise is immediately available to less experienced first tier agents. More problems are solved in the first tier and they are solved more rapidly due to the efficient use of computerized expertise.
- Planning, scheduling, assembling and designing:For example, a bill of materials and cost estimate can be generated by matching a new set of specifications to past projects that were similar.
- Diagnosis:A set of symptoms is matched against known examples. Diagnosis includes machine faults and process deviations, as well as medical applications.
- Legal Reasoning:Legal reasoning is heavily based on precedent. CBR uses past cases to aid in accessing and displaying these precedents.
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. A lawyertrial based on legal precedents or a judge who creates case law is using case-based reasoning. So, too, an engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems. Case-based reasoning is a prominent kind of analogy making. who advocates a particular outcome in a
It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving. Or, more radically, that all reasoning is based on past cases experienced or accepted by the being actively exercising choice – prototype theory – most deeply explored in human cognitive science.
Artificial Intelligence has long been considered as fruit of our intentions to realize nature in our mechanized- human lives; just as aero planes were, of our desire to fly with like the birds of flight. Case-based reasoning (CBR), 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. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning. Case-based reasoning is a prominent kind of analogy making.It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving. Or, more radically, that all reasoning is based on past cases experienced or accepted by the being actively exercising choice – prototype theory – most deeply explored in human cognitive science.
CBR technology has produced a number of successful deployed systems, the earliest being Lockheed s CLAVIER, a system for laying out composite parts to be baked in an idustrial convection oven. CBR has been used extensively in help-desk applications such as the Compaq SMART system. As of this writing, a number of CBR decision support tools are commercially available, including k-Commerce from eGain (formerly Inference Corporation), Kaidara Advisor from Kaidara (formerly AcknoSoft) and SMART from Illation.
Case-based reasoning has been formalized for purposes of computer reasoning as a four-step process:
- Retrieve: Given a target problem, retrieve cases from memory that are relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case.
- Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries.
- Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue – an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan.
- Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his newfound procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands.
