5.4 Case-based reasoning for problem-solving

Case-based reasoning is a problem-solving approach that uses past experiences to tackle new challenges. It's like having a wise friend who remembers similar situations and offers advice. The process involves finding similar cases, adapting solutions, and learning from new experiences.

This method fits into knowledge representation by storing and using real-world examples. Unlike rule-based systems that follow strict guidelines, case-based reasoning can handle unique situations by drawing on past experiences. It's a flexible way to apply knowledge to new problems.

Case-based reasoning concepts

CBR process and assumptions

Top images from around the web for CBR process and assumptions

• 

  Philosophy Of Thought And Logic 2012 - The Collaboratory View original

  Is this image relevant?

• 

  Category:Case-based reasoning - Wikimedia Commons View original

  Is this image relevant?

• 

  Philosophy Of Thought And Logic 2012 - The Collaboratory View original

  Is this image relevant?

• 

  Category:Case-based reasoning - Wikimedia Commons View original

  Is this image relevant?

1 of 2

Top images from around the web for CBR process and assumptions

• 

  Philosophy Of Thought And Logic 2012 - The Collaboratory View original

  Is this image relevant?

• 

  Category:Case-based reasoning - Wikimedia Commons View original

  Is this image relevant?

• 

  Philosophy Of Thought And Logic 2012 - The Collaboratory View original

  Is this image relevant?

• 

  Category:Case-based reasoning - Wikimedia Commons View original

  Is this image relevant?

1 of 2

• Case-based reasoning (CBR) is a problem-solving approach that relies on the reuse of past experiences, known as cases, to solve new problems
• The CBR process involves four main steps: retrieval, reuse, revision, and retention (the 4 Rs)
  • Retrieval involves finding the most similar case(s) to the current problem from a case base
  • Reuse involves adapting the solution from the retrieved case(s) to fit the current problem
  • Revision involves evaluating the proposed solution and modifying it if necessary
  • Retention involves storing the new problem and its solution as a new case in the case base for future use
• CBR is based on the assumption that similar problems have similar solutions, and that past experiences can guide problem-solving in new situations (e.g., diagnosing a medical condition based on similar patient cases)
• The effectiveness of CBR depends on the quality and relevance of the cases in the case base, as well as the ability to retrieve and adapt appropriate cases for new problems

Case representation and organization

• Cases in a CBR system can be represented using various formats, such as feature-value pairs, structured representations, or textual descriptions
  • Feature-value pairs represent cases as a set of attributes and their corresponding values (e.g., car make, model, year, and price)
  • Structured representations organize case information into predefined categories or hierarchies (e.g., patient demographics, symptoms, test results, and diagnosis)
  • Textual descriptions capture case details in natural language format (e.g., customer support inquiries and solutions)
• The organization of the case base, such as indexing and clustering, can impact the efficiency of case retrieval
  • Indexing assigns labels or tags to cases based on relevant features, allowing for quick retrieval of similar cases (e.g., indexing legal cases by legal principles or key facts)
  • Clustering groups similar cases together based on their features, enabling efficient retrieval of related cases (e.g., clustering design cases by product category or functionality)

Case-based reasoning systems

System components

• A case-based reasoning system typically consists of four main components: a case base, a retrieval mechanism, an adaptation mechanism, and a retention mechanism
• The case base is a repository of past problem-solving experiences, where each case typically includes a problem description, a solution, and an outcome
• The retrieval mechanism is responsible for finding the most similar case(s) to the current problem, often using similarity measures and search algorithms
  • Similarity measures, such as nearest-neighbor or induction algorithms, assess the relevance of cases based on problem features (e.g., comparing patient symptoms using Euclidean distance)
  • Search algorithms, such as k-nearest neighbor or decision trees, efficiently navigate the case base to find the most similar cases (e.g., using a k-d tree to locate the k most similar design cases)
• The adaptation mechanism modifies the solution from the retrieved case(s) to fit the current problem, taking into account differences between the problems
  • Adaptation can be achieved through methods such as substitution, transformation, or generative adaptation (e.g., substituting ingredients in a recipe based on dietary restrictions)
  • Domain knowledge, such as rules or constraints, can guide the adaptation process to ensure solution validity (e.g., applying legal principles to adapt a legal argument)
• The retention mechanism incorporates the new problem and its solution into the case base, allowing the system to learn from new experiences

Maintenance and optimization

• Retention strategies, such as selective retention or forgetting, manage the growth of the case base and maintain its quality
  • Selective retention adds only informative or diverse cases to the case base, preventing redundancy and improving retrieval efficiency (e.g., retaining customer support cases that introduce new problem-solving patterns)
  • Forgetting removes outdated, irrelevant, or low-quality cases from the case base, ensuring the system remains up-to-date and efficient (e.g., removing legal cases that are no longer applicable due to changes in legislation)
• Maintenance techniques, such as case deletion or clustering, optimize the organization and efficiency of the case base over time
  • Case deletion removes cases that are no longer useful or relevant, freeing up storage space and improving retrieval speed (e.g., deleting old product designs that are no longer in production)
  • Case clustering reorganizes the case base by grouping similar cases together, enabling faster retrieval and adaptation (e.g., clustering medical cases by disease type or severity)

Case-based reasoning applications

Domain-specific examples

• CBR has been successfully applied to various domains, such as medical diagnosis, customer support, design problem-solving, and legal reasoning
• In medical diagnosis, CBR systems can assist physicians by retrieving similar patient cases and suggesting potential diagnoses or treatment plans
  • Medical case bases can include patient symptoms, test results, diagnoses, and treatment outcomes (e.g., a case base of rare genetic disorders and their associated symptoms and treatments)
  • Retrieval and adaptation mechanisms consider the similarity of patient profiles and adapt treatment plans based on individual characteristics (e.g., adjusting medication dosages based on patient age and weight)
• In customer support, CBR systems can help resolve customer inquiries by retrieving similar past cases and suggesting solutions
  • Customer support case bases can include problem descriptions, solution steps, and customer feedback (e.g., a case base of common technical issues and their resolutions for a software product)
  • Retrieval mechanisms can match customer queries with relevant cases, while adaptation mechanisms tailor solutions to specific customer contexts (e.g., adapting troubleshooting steps based on the customer's device and operating system)
• In design problem-solving, CBR systems can aid designers by retrieving similar design cases and suggesting design modifications or alternatives
  • Design case bases can include design specifications, constraints, solutions, and performance metrics (e.g., a case base of architectural designs for energy-efficient buildings)
  • Retrieval and adaptation mechanisms can identify relevant design cases and propose design changes based on new requirements or constraints (e.g., adapting a building design to accommodate a different climate or site layout)
• In legal reasoning, CBR systems can support legal decision-making by retrieving similar legal cases and suggesting arguments or precedents
  • Legal case bases can include case facts, legal principles, arguments, and decisions (e.g., a case base of intellectual property disputes and their outcomes)
  • Retrieval mechanisms can find relevant legal cases based on case similarities, while adaptation mechanisms can apply legal principles to new situations (e.g., adapting a legal argument to a new jurisdiction or legal context)

Considerations for real-world application

• When applying CBR to real-world problems, it is important to consider the characteristics of the domain, the available data, and the specific requirements of the problem-solving task
  • Domain knowledge, such as problem features, solution constraints, and evaluation criteria, should be carefully modeled and incorporated into the CBR system (e.g., capturing relevant medical knowledge for diagnosis and treatment recommendation)
  • Data quality, including the representativeness and completeness of cases, should be assessed and addressed to ensure reliable problem-solving performance (e.g., ensuring that customer support cases cover a wide range of problem scenarios)
  • The CBR system should be evaluated and validated using appropriate metrics and benchmarks, considering factors such as retrieval accuracy, adaptation quality, and user satisfaction (e.g., measuring the precision and recall of legal case retrieval and gathering feedback from legal professionals)

Case-based reasoning vs other approaches

Comparison with rule-based and model-based reasoning

• Compared to rule-based reasoning, which relies on explicit domain knowledge in the form of rules, CBR leverages past experiences and can handle novel or exceptional cases
  • Rule-based systems require extensive knowledge engineering to capture domain rules, while CBR can learn from examples without explicit rule formulation (e.g., a rule-based system for medical diagnosis would require manually encoding diagnostic rules, while a CBR system can learn from past patient cases)
  • CBR can provide solutions to problems that do not strictly match predefined rules, while rule-based systems may struggle with exceptional or unanticipated cases (e.g., a rule-based system for customer support may not have a rule for a unique customer problem, while a CBR system can retrieve and adapt a solution from a similar past case)
• Compared to model-based reasoning, which uses explicit domain models to simulate problem scenarios, CBR relies on past cases and can handle problems with incomplete or uncertain information
  • Model-based systems require accurate and complete domain models, while CBR can operate with partial or imprecise case data (e.g., a model-based system for design problem-solving would require a comprehensive model of the design space, while a CBR system can work with incomplete or inconsistent design cases)
  • CBR can provide solutions based on similar past experiences, while model-based systems generate solutions through simulation and inference (e.g., a model-based system for legal reasoning would simulate legal scenarios based on a legal domain model, while a CBR system would retrieve and adapt solutions from similar past legal cases)

Comparison with machine learning approaches

• Compared to machine learning approaches, such as neural networks or decision trees, CBR provides explanatory power and can handle small or incremental case bases
  • Machine learning methods typically require large training datasets and can produce opaque models, while CBR can work with limited case data and provide transparent reasoning (e.g., a neural network for customer support would require a large dataset of customer inquiries and solutions, while a CBR system can start with a small set of representative cases and provide explanations for its recommendations)
  • CBR can incrementally learn from new cases without retraining the entire system, while machine learning models often require retraining when new data becomes available (e.g., a decision tree for medical diagnosis would need to be retrained whenever new patient cases are added, while a CBR system can simply add new cases to its case base without retraining)

Factors influencing the choice of approach

• The choice of problem-solving approach depends on factors such as the availability of domain knowledge, the complexity of the problem space, the interpretability requirements, and the scalability needs
  • CBR is well-suited for domains with limited formalized knowledge, complex problem spaces, and a need for explainable reasoning (e.g., legal reasoning, where cases are complex and explanations are crucial)
  • Rule-based or model-based approaches are preferred when domain knowledge is well-structured, and the problem space is clearly defined (e.g., diagnosing simple medical conditions based on well-established diagnostic criteria)
  • Machine learning is effective for problems with large datasets, complex patterns, and a focus on predictive accuracy over interpretability (e.g., predicting customer churn based on large volumes of customer data)
• In practice, hybrid approaches that combine CBR with other problem-solving techniques can leverage the strengths of each approach and mitigate their limitations
  • CBR can be combined with rule-based reasoning to handle exceptions and provide case-based explanations (e.g., using rules to filter irrelevant cases and provide initial solutions, while using CBR to handle exceptional cases and provide explanations)
  • CBR can be integrated with model-based reasoning to guide case adaptation and validate solutions (e.g., using a domain model to simulate the adapted solution and check its feasibility)
  • CBR can be enhanced with machine learning techniques to improve case retrieval, adaptation, and maintenance (e.g., using clustering algorithms to organize the case base and using learning-to-rank methods to improve case retrieval)