By Janet L. Kolodner (auth.), Janet L. Kolodner (eds.)
ISBN-10: 1461364183
ISBN-13: 9781461364184
ISBN-10: 1461532280
ISBN-13: 9781461532286
Case-based reasoning capacity reasoning in line with remembering past reports. A reasoner utilizing previous reports (cases) could use these situations to indicate options to difficulties, to show power issues of an answer being computed, to interpret a brand new state of affairs and make predictions approximately what could occur, or to create arguments justifying a few end. A case-based reasoner solves new difficulties by way of remembering previous occasions and adapting their options. It translates new events by means of remembering previous comparable events and evaluating and contrasting the recent one to outdated ones to determine the place it matches most sensible. Case-based reasoning combines reasoning with studying. It spans the entire reasoning cycle. A scenario is skilled. outdated events are used to appreciate it. outdated events are used to unravel an issue (if there's one to be solved). Then the hot state of affairs is inserted into reminiscence along the instances it used for reasoning, for use one other time.
the main to this reasoning strategy, then, is remembering. Remembering has components: integrating situations or reports into reminiscence once they ensue and recalling them in applicable occasions in a while. The case-based reasoning group calls this similar set of concerns the indexing problem. In large phrases, it capacity discovering in reminiscence the event closest to a brand new scenario. In narrower phrases, it may be defined as a two-part challenge:
- assigning indexes or labels to stories after they are placed into reminiscence that describe the occasions to which they're acceptable, as a way to be recalled later; and
- at keep in mind time, elaborating the hot scenario in sufficient element in order that the indexes it can have if it have been within the reminiscence are pointed out.
Case-Based Learning is an edited quantity of unique study comprising invited contributions by means of best staff. This paintings has additionally been released as a distinct problems with MACHINE LEARNING, quantity 10, No. 3.
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Sample text
2) The blackmailer has a goal G2, and the blackmailee does not have the goal G2 (since otherwise he or she would satisfy the goal without needing to be threatened). (3) The blackmailee has a goal G3, which he or she values above goal G1. (4) The blackmailer threatens to violate G3 unless the blackmailee performs an action A that satisfies G2, even though the action would have a negative effect of violating G 1. Figure 6. Internals of the blackmail explanation pattern. 30 INDEXING, ELABORATION AND REFINEMENT 225 Table 4.
AQUA learns the concepts that are useful in performing this task. One of the claims of our theory of question-driven understanding is that asking good questions is as important to understanding as is answering them (Ram, 1989; Ram, 1991). Learning the questions to ask when the case is next applied is a central issue since this allows the system to reason about what it does not yet know but needs to find out. These questions focus the understanding process during future stories that might answer the questions.
Generalize si to yield an answer S to the original question whose concept specification was g. • Merge S with g in XP, and remove the tag identifying this node as a gap. b I ackma i I-su i c ide-bomb i ng), based on the particular manner in which xp-b I ackma i I was adapted to fit the story. AQUA also learns indices to the new XP. Both kinds of learning are important in a case-based reasoning system (Ram, 1990c). The algorithm for incremental case modification and elaboration is shown in table 4.
Case-Based Learning by Janet L. Kolodner (auth.), Janet L. Kolodner (eds.)
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