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Learning automata solutions to enhan...
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Carleton University (Canada).
Learning automata solutions to enhancing optimal search for unknown target distributions.
紀錄類型:
書目-電子資源 : 單行本
正題名/作者:
Learning automata solutions to enhancing optimal search for unknown target distributions./
作者:
Ellaithy, Amr.
面頁冊數:
114 p.
附註:
Source: Masters Abstracts International, Volume: 42-01, page: 0257.
Contained By:
Masters Abstracts International42-01.
標題:
Computer Science. -
電子資源:
Download fulltext (下載全文)
ISBN:
0612797643
Learning automata solutions to enhancing optimal search for unknown target distributions.
Ellaithy, Amr.
Learning automata solutions to enhancing optimal search for unknown target distributions.
- 114 p.
Source: Masters Abstracts International, Volume: 42-01, page: 0257.
Thesis (M.C.S.)--Carleton University (Canada), 2003.
We consider the problem of locating an object that is hidden in one of N possible locations (or bins). The importance of the field of searching for hidden objects in a specified amount of time is evident in problems involving large databases and in military applications. The intention is to allocate the available resources so as to maximize the probability of locating the object, and also to minimize the cost of search. The locations are identified as {C1,...,CN}, with the probability that the object is present at location Ci being p(i).
ISBN: 0612797643Subjects--Topical Terms:
1000005419
Computer Science.
Learning automata solutions to enhancing optimal search for unknown target distributions.
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Learning automata solutions to enhancing optimal search for unknown target distributions.
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114 p.
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Source: Masters Abstracts International, Volume: 42-01, page: 0257.
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Adviser: B. John Oommen.
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Thesis (M.C.S.)--Carleton University (Canada), 2003.
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We consider the problem of locating an object that is hidden in one of N possible locations (or bins). The importance of the field of searching for hidden objects in a specified amount of time is evident in problems involving large databases and in military applications. The intention is to allocate the available resources so as to maximize the probability of locating the object, and also to minimize the cost of search. The locations are identified as {C1,...,CN}, with the probability that the object is present at location Ci being p(i).
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Unlike the problem addressed by many researchers, we assume that the set of all probabilities, {p(i)}, referred to as the Target distribution, is unknown. Research in this new scenario was recently initiated by Zhu and Oommen, and depends on assuming a target distribution, using certain guidelines, which, in turn, could help in allocating the resources and locating the hidden target. In this Thesis we will focus on methods by which we can "guess" this assumed target distribution in real-life scenarios. The methods chosen are efficient, accurate and also valid as per the guidelines laid out in the previous research. To achieve this, we have made use of the field of learning automata, and in particular, of some recent results by Oommen and Raghunath, which have been central for developing the process of guessing the target distribution. The Thesis can thus be seen as a novel contribution that has evolved by merging new results in two completely distinct areas.
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