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In what situations is Case Based Reasoning appropriate?
Case-based reasoning is used for classification and for regression. It is also applicable when the cases are complicated, such as in legal cases, where the cases are complex legal rulings, and in planning, where the cases are previous solutions to complex problems.
Which are the following applications can be solved using Case Based Reasoning?
Applications of CBR includes: Problem resolution for customer service help desks, where cases describe product-related diagnostic problems. It is also applied to areas such as engineering and law, where cases are either technical designs or legal rulings, respectively.
How Case Based Reasoning is different?
Case-based reasoning has several differences from other AI approaches, such as knowledge-based systems (KBS). CBR also offers incremental, sustained learning in that each time a problem is solved a new experience is retained and can be applied for future problems.
Why Case Based Reasoning is called as lazy learner?
The benefits of CBR as a lazy problem-solving method are: Absence of problem-solving bias: Because cases are stored in a “raw” form, they can be used for multiple problem-solving purposes. This in contrast to eager methods, which can be used merely for the purpose for which the knowledge has already been compiled.
What are four popular types of intelligent agents?
Agents can be grouped into four classes based on their degree of perceived intelligence and capability :
- Simple Reflex Agents.
- Model-Based Reflex Agents.
- Goal-Based Agents.
- Utility-Based Agents.
- Learning Agent.
Why is case-based learning good?
Advantages of using CBL include more focusing on learning objectives compared with PBL, flexibility on the use of the case, and ability to induce a deeper level of learning by inducing more critical thinking skills.
What are two applications of case-based reasoning?
Applications of Computers and Information Technology At present, prototype computer programs are being tested for their effectiveness in problem-solving tasks using the case-based reasoning approach. Manual systems have been successfully used in medicine, law, and in design and failure analysis.
What is case-based learning in ML?
Case-based learning (CBL) is an established approach used across disciplines where students apply their knowledge to real-world scenarios, promoting higher levels of cognition (see Bloom’s Taxonomy).
What is lazy learners give an example?
Lazylearning refers to any machine learning process that defers the majority of computation to consultation time. Two typical examples of lazy learning are instance-based learning and Lazy Bayesian Rules. Lazy learning stands in contrast to eager learning in which the majority of computation occurs at training time.