By Janet L. Kolodner (auth.), Janet L. Kolodner (eds.)
Case-based reasoning ability reasoning in accordance with remembering prior reports. A reasoner utilizing outdated studies (cases) may use these instances to indicate recommendations to difficulties, to indicate strength issues of an answer being computed, to interpret a brand new state of affairs and make predictions approximately what may possibly ensue, or to create arguments justifying a few end. A case-based reasoner solves new difficulties via remembering previous events and adapting their suggestions. It translates new occasions via remembering outdated related events and evaluating and contrasting the recent one to previous ones to determine the place it matches top. Case-based reasoning combines reasoning with studying. It spans the total reasoning cycle. A state of affairs is skilled. outdated events are used to appreciate it. previous events are used to unravel an issue (if there's one to be solved). Then the recent state of affairs is inserted into reminiscence along the situations it used for reasoning, for use one other time.
the most important to this reasoning procedure, then, is remembering. Remembering has components: integrating instances or stories into reminiscence after they occur and recalling them in acceptable events in a while. The case-based reasoning group calls this comparable set of concerns the indexing problem. In huge phrases, it capability 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 studies once they are positioned into reminiscence that describe the occasions to which they're appropriate, a good way to be recalled later; and
- at remember time, elaborating the hot scenario in sufficient aspect in order that the indexes it's 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 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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Extra resources for Case-Based Learning
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. Here again there are no "right" questions, only "better" ones (those that help the system to learn) and "worse" ones (those that miss the point of the input stories).
Question generation Input: XP, an explanation pattern; XPi , the instantiation of XP for the situation at hand; independent confirmation for XPi . Output: XP', an elaborated version of XP with one or more gaps g identified. Algorithm: • Confirm XPi in the hypothesis tree and refute its competitors . • Copy XP to XP'. • v HVQs of XPi that are not answered, mark the corresponding XP-ASSERTED-NODE gin XP' as a gap. Install a question whose concept specification is g (question generation through identification of new gaps).
When this story is read, AQUA retrieves the new xp- b I ackma i 1- su i c i de- bomb i ng (figure 7) and applies it to the story. The question that is pending along with this explanation is also instantiated. When the question is answered, it is replaced by a new node representing the protect-fami Iy goal, and becomes part of xp-blackmai I-suicidebomb i ng (figure 8). Since no explanations are known for the newly added node, this in tum becomes a new question about the elaborated XP (not shown in the figure).
Case-Based Learning by Janet L. Kolodner (auth.), Janet L. Kolodner (eds.)