Editing Case-based reasoning (section)

== Comparison to other methods ==
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At first glance, CBR may seem similar to the [[rule induction]] [[algorithm]]s<ref name="ref_ruleind" group="note">Rule-induction algorithms are procedures for learning rules for a given concept by generalizing from examples of that concept.  For example, a rule-induction algorithm might learn rules for forming the plural of English nouns from examples such as ''dog/dogs'', ''fly/flies'', and ''ray/rays''.</ref> of [[machine learning]]. Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem.<ref>{{cite book |last1=Richter |first1=Michael M. |last2=Weber |first2=Rosina O. |date=2013 |title=Case-based reasoning: a textbook |location=Heidelberg |publisher=[[Springer-Verlag]] |isbn=9783642401664 |oclc=857646182 |doi=10.1007/978-3-642-40167-1|s2cid=6295943 }}</ref>

If for instance a procedure for plain pancakes is mapped to blueberry pancakes, a decision is made to use the same basic batter and frying method, thus implicitly generalizing the set of situations under which the batter and frying method can be used. The key difference, however, between the implicit generalization in CBR and the generalization in rule induction lies in when the generalization is made. A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization.

For instance, if a rule-induction algorithm were given recipes for plain pancakes, Dutch apple pancakes, and banana pancakes as its training examples, it would have to derive, at training time, a set of general rules for making all types of pancakes. It would not be until testing time that it would be given, say, the task of cooking blueberry pancakes. The difficulty for the rule-induction algorithm is in anticipating the different directions in which it should attempt to generalize its training examples. This is in contrast to CBR, which delays (implicit) generalization of its cases until testing time – a strategy of lazy generalization. In the pancake example, CBR has already been given the target problem of cooking blueberry pancakes; thus it can generalize its cases exactly as needed to cover this situation. CBR therefore tends to be a good approach for rich, complex domains in which there are myriad ways to generalize a case.

In law, there is often explicit delegation of CBR to courts, recognizing the limits of rule based reasons: limiting delay, limited knowledge of future context, limit of negotiated agreement, etc. While CBR in law and cognitively inspired CBR have long been associated, the former is more clearly an interpolation of rule based reasoning, and judgment, while the latter is more closely tied to recall and process adaptation. The difference is clear in their attitude toward error and appellate review.

Another name for cased based reasoning in problem solving is symptomatic strategies. It does require à priori domain knowledge that is gleaned from past experience which established connections between symptoms and causes. This knowledge is referred to as shallow, compiled, evidential, history-based as well as case-based knowledge. This is the strategy most associated with diagnosis by experts. Diagnosis of a problem transpires as a rapid recognition process in which symptoms evoke appropriate situation categories.<ref name="Gilhooly">Gilhooly, Kenneth J. "Cognitive psychology and medical diagnosis." Applied cognitive psychology 4.4 (1990): 261-272.</ref> An expert knows the cause by virtue of having previously encountered similar cases. Cased based reasoning is the most powerful strategy, and that used most commonly. However, the strategy won't work independently with truly novel problems, or where deeper understanding of whatever is taking place is sought.

An alternative approach to problem solving is the topographic strategy which falls into the category of deep reasoning. With deep reasoning, in-depth knowledge of a system is used. Topography in this context means a description or an analysis of a structured entity, showing the relations among its elements.<ref name="American">American Heritage Dictionary.</ref>

Also known as reasoning from first principles,<ref name="Davis">Davis, Randall. "Reasoning from first principles in electronic troubleshooting." International Journal of Man-Machine Studies 19.5 (1983): 403-423.</ref> deep reasoning is applied to novel faults when experience-based approaches aren't viable. The topographic strategy is therefore linked to à priori domain knowledge that is developed from a more a fundamental understanding of a system, possibly using first-principles knowledge. Such knowledge is referred to as deep, causal or model-based knowledge.<ref name=" Milne">Milne, Robert. "Strategies for diagnosis." IEEE transactions on systems, man, and cybernetics 17.3 (1987): 333-339.</ref> 
Hoc and Carlier<ref name="Hoc">Hoc, Jean-Michel. "A method to describe human diagnostic strategies in relation to the design of human-machine cooperation." International Journal of Cognitive Ergonomics 4.4 (2000): 297-309.</ref> noted that symptomatic approaches may need to be supported by topographic approaches because symptoms can be defined in diverse terms. The converse is also true – shallow reasoning can be used abductively to generate causal hypotheses, and deductively to evaluate those hypotheses, in a topographical search.