Case-Based Learning by Janet L. Kolodner (auth.), Janet L. Kolodner (eds.)

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By Janet L. Kolodner (auth.), Janet L. Kolodner (eds.)

Case-based reasoning skill reasoning in accordance with remembering prior stories. A reasoner utilizing previous reports (cases) may well use these situations to signify strategies to difficulties, to indicate power issues of an answer being computed, to interpret a brand new scenario and make predictions approximately what may well take place, or to create arguments justifying a few end. A case-based reasoner solves new difficulties via remembering outdated occasions and adapting their suggestions. It translates new events by means of remembering outdated comparable occasions and evaluating and contrasting the recent one to outdated ones to determine the place it suits most sensible. Case-based reasoning combines reasoning with studying. It spans the total reasoning cycle. A scenario is skilled. previous occasions are used to appreciate it. previous occasions are used to unravel an issue (if there's one to be solved). Then the recent scenario is inserted into reminiscence along the circumstances it used for reasoning, for use one other time.
the foremost to this reasoning procedure, then, is remembering. Remembering has components: integrating situations or studies into reminiscence once they occur and recalling them in acceptable events in a while. The case-based reasoning neighborhood calls this comparable set of concerns the indexing problem. In huge phrases, it potential discovering in reminiscence the event closest to a brand new state of affairs. In narrower phrases, it may be defined as a two-part challenge:

  • assigning indexes or labels to studies once they are placed into reminiscence that describe the occasions to which they're acceptable, with a view to be recalled later; and
  • at remember time, elaborating the recent state of affairs in adequate element in order that the indexes it is going to have if it have been within the reminiscence are pointed out.

Case-Based Learning is an edited quantity of unique learn comprising invited contributions by way of prime staff. This paintings has additionally been released as a different problems with MACHINE LEARNING, quantity 10, No. 3.

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A node in the XP may be replaced by a collection of nodes, representing a more detailed understanding of that part of the causal structure of the XP. An XP may also be elaborated by replacing one of the LINKS with a set of LINKS between the same nodes, with new intermediate nodes being added in between these nodes. For example, a LINK specifying a direct causal relationship between two nodes ni and n2 may be replaced by a more refined LINK that invokes an XP in which n2 is the EXPLAINS node and ni is one of the XPASSElUED-NODES.

Firstly, it illustrates a deeper understanding of the domain since it results from the elaborated coercion hypothesis shown in figure 8. This hypothesis often occurs to people as a possible explanation for story S-6 after reading these stories in succession. Secondly, from the computational point of view, asking the question sets up an expectation that makes it easier for the system to understand the story if the question does tum out to be relevant. ) Suppose the story is modified to continue as follows: 8-6: (continued) In a statement issued the next day, [the driver's] family said that their son loved them very much and would do anything for their sake.

For example, AQUA does not reason about the possibility that blackmail may in fact not be a possible explanation for suicide bombing, since it has read a story in which it was stated to be the explanation for an instance of suicide bombing. 15. Comparison with other work In this article, we have presented three classes of reasoning difficulties-those arising from missing, mis-indexed, and incomplete knowledge-that may be encountered during casebased reasoning, and we have presented algorithms that allow a reasoner to learn through experience with situations involving these difficulties.

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