What is case-based reasoning?

Case-based reasoning (CBR) is an AI meth­od­o­logy that addresses problems by lever­aging past ex­per­i­ences to tackle chal­lenges ef­fi­ciently. It adapts proven solutions to current situ­ations through a four-step process. Common ap­plic­a­tions of CBR include help desk systems and medical therapy re­com­mend­a­tion tools.

What does case-based reasoning mean?

Case-based reasoning is a machine learning method in the field of ar­ti­fi­cial in­tel­li­gence. This approach uses past ex­per­i­ences to solve new but similar problems, relying on analogy. The core idea of this ex­per­i­ence-based method is that similar problems tend to have similar solutions. Instead of analysing each problem from scratch, the meth­od­o­logy leverages a database of pre­vi­ously solved cases. These cases act as ref­er­ences to identify suitable solutions for current chal­lenges.

Case-based reasoning forms the found­a­tion of a novel approach to machine learning, that enables computer systems to adapt to new situ­ations. This problem-solving approach ori­gin­ated from the research of Roger Schank, an American com­mu­nic­a­tion and computer scientist, and his students in the 1980s. Their work explored human episodic memory, revealing that suc­cess­ful problem-solving fre­quently draws on past ex­per­i­ences from com­par­able situ­ations.

How does case-based reasoning work?

Case-based reasoning is a process that usually consists of four steps:

1. Retrieve: The CBR system examines the case database to find ex­per­i­ences most similar to the current problem based on its de­scrip­tion.
2. Reuse: As a problem-solving approach, the solution of the case that is most similar to the de­scrip­tion of the problem is used first. The first approach serves as the starting point for working on the new problem.
3. Revise: The proposed solution is tested in the new context and refined as needed to fit specific con­di­tions. Ad­just­ments or cor­rec­tions are made if required.
4. Retain: The new problem-solving method is added to the case base for future requests. This creates an in­cre­ment­al learning process that ensures that the per­form­ance of the process increases with every case solved.

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What are the main areas of ap­plic­a­tion for CBR?

Since case-based reasoning relies on the in­tel­li­gent reuse of previous solutions, it is par­tic­u­larly useful in situ­ations where patterns can be iden­ti­fied and similar chal­lenges occur re­peatedly. However, CBR is also well-suited for poorly struc­tured problems, those with in­com­plete de­scrip­tions, or scenarios where precise knowledge of causal re­la­tion­ships is lacking. Unlike other AI methods, CBR systems can perform ef­fect­ively with a small number of reference cases. Common ap­plic­a­tion areas include:

• Medical dia­gnostics: CBR is used to analyse previous dia­gnost­ic cases based on patient data and to identify diagnoses or suitable thera­peut­ic ap­proaches for the in­di­vidu­als currently being treated.
• Troubleshoot­ing of technical systems: In IT support and system main­ten­ance, CBR helps identify solutions quickly. A robust case database can also enable early fault detection to prevent sig­ni­fic­ant damage.
• Customer service: CBR systems are also used to answer support queries by referring to tried and tested solutions.
• Product advice systems: In eCommerce, CBR is in­creas­ingly used to recommend products to potential buyers based on previous customer pref­er­ences.

CBR has many other ap­plic­a­tions. In finance, it can assist in­sti­tu­tions in making decisions about loan approvals, risk as­sess­ments, and eval­u­at­ing in­vest­ment strategies. In the legal field, CBR tools analyse previous court cases to develop arguments for new cases. In trans­port­a­tion and logistics, CBR is used for route planning and resource al­loc­a­tion.

What role does CBR play in relation to AI?

Case-based reasoning plays an important role in the field of ar­ti­fi­cial in­tel­li­gence, as it provides a meth­od­o­lo­gic­al basis for sim­u­lat­ing human-like problem-solving behaviour. While other AI tech­niques such as neural networks are based on the pro­cessing of large amounts of data, CBR uses ex­per­i­ence in the form of past cases to solve new problems. Case-based reasoning also makes it possible to con­tinu­ously improve AI systems and make them more robust and adaptable. Finally, the system expands its knowledge base with each new case.

What are the ad­vant­ages and dis­ad­vant­ages of case-based reasoning?

The case-based reasoning learning method offers numerous ad­vant­ages. The most important ad­vant­ages include

• Ef­fi­ciency through the reuse of knowledge: Reusing previous cases as templates reduces the time and cost of analysing problems from scratch, often leading to more efficient solutions.
• Learn­ab­il­ity: CBR systems improve their problem-solving abilities over time as they learn from new cases and expand their database.
• High flex­ib­il­ity: By selecting relevant cases, case-based reasoning is able to adapt to different situ­ations and contexts. This is also the reason why CBR can be used in numerous domains.
• Ex­plain­ab­il­ity: Since solutions are based on previous cases, CBR is also able to provide trans­par­ent ex­plan­a­tions for certain solution ap­proaches. This is par­tic­u­larly useful in areas where the trace­ab­il­ity of decisions is of great im­port­ance.
• Intuitive approach: Since case-based reasoning is based on human problem-solving strategies, it is easy to un­der­stand how solutions are reached.

However, CBR systems also have some dis­ad­vant­ages:

• De­pend­ence on data quality: The ef­fi­ciency of a CBR system relies heavily on the quality and com­plete­ness of the case base. If the stored cases are in­ac­cur­ate or in­com­plete, the system may produce sub­op­tim­al solutions.
• Problems with scalab­il­ity: As the size of the case base increases, it may take sig­ni­fic­antly longer to retrieve and adjust cases, reducing the ef­fi­ciency of the system.
• Case cus­tom­isa­tion is sometimes difficult: Adapting previous cases to new problems can be difficult. Therefore, it requires soph­ist­ic­ated al­gorithms to ensure that ad­just­ments are both mean­ing­ful and effective.
• Risk of ob­sol­es­cence: Solutions that worked well in the past may become ir­rel­ev­ant over time, es­pe­cially in rapidly evolving fields. In some cases, this could lead to outdated solutions being proposed.