Question

Which among the following is not a challenge for case based reasoning implementation?

(a) Memory

(b) Fixed ontology and knowledge representation

(c) Pattern matching

(d) Accuracy of reasoning

This question was posed to me in an interview for internship.

This intriguing question originated from Next Generation Wireless Network topic in chapter Next Generation Wireless Network of Cognitive Radio

Answer 1

The correct answer is:

(b) Fixed ontology and knowledge representation

Explanation:

Case-based reasoning (CBR) is a problem-solving approach that relies on using past cases (or experiences) to solve new problems. The challenges typically associated with CBR are:

• Memory (option a) refers to the ability to store and retrieve previous cases effectively. This can be a challenge in terms of how much data can be stored and how quickly relevant cases can be retrieved.
• Pattern matching (option c) is an essential part of CBR. It involves finding the most similar past cases to the current problem. This can be computationally challenging, especially with a large number of cases.
• Accuracy of reasoning (option d) refers to how well the system can apply past experiences to new situations, which can be difficult if the retrieved cases are not perfectly aligned with the new problem.

On the other hand, fixed ontology and knowledge representation (option b) is not typically a challenge for CBR. CBR is flexible and does not require a rigid, pre-defined ontology or formal knowledge representation structure, unlike other AI techniques that rely heavily on such fixed frameworks (like expert systems). CBR focuses on the retrieval of past cases and adapting them to new situations, and the representation of knowledge can be more fluid or case-specific.

Thus, fixed ontology and knowledge representation is the least relevant challenge for implementing case-based reasoning.