Knowledge Management and Acquisition for Smart Systems and by Debbie Richards, Byeong-Ho Kang

By Debbie Richards, Byeong-Ho Kang

The booklet constitutes the completely refereed lawsuits of the eleventh foreign Workshop on wisdom administration and Acquisition for shrewdpermanent structures and companies, held in Daegue, Korea in August 2010 along with the Pacific Rim overseas convention on man made Intelligence, PRICAI 2010. The 26 revised complete papers have been chosen from ninety four submissions and are prepared in topical sections on computer studying, information Mining, wisdom Engineering & Ontology, Incremental wisdom Acquisition, KA purposes in net and cellular Computing and KA functions in Multimedia and video games.

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Extra resources for Knowledge Management and Acquisition for Smart Systems and Services: 11th International Workshop, PKAW 2010, Daegue, Korea, August 30 - 31, 2010, Proceedings ...

Example text

3-Partition. , sn } of n = 3p positive integers, can S be partitioned into p subsets S1 , S2 , Sp such that the sum of the numbers in each subset is equal? [2] Given any instance of 3-Partition, consider the following instance of SISHK, with n students who have attended one subject only. Each student i for 1 ≤ i ≤ n has a score si for this subject. It is easy to see that the instance of 3-Partition has a feasible partition if and only if the instance of SISHK has a partition with (7) equal zero, if and only if A returns a solution to the instance of SISHK with (7) equal zero.

Following are showed the training values used in the network: Table 3. Neural Network training set Number of input data 4061 ( > 16 Years of Financial Data) Cross Validation 20% of Input Data Training 10% of Input Data The number of epochs set in order to train the network was specified to 10,000 epochs establishing that the network must stop learning after 500 epochs without improvement in cross-validation error. 2 Trading System Module This module analyzes the result given by neural network module.

The x-aix represents the number of simulation task allocation runs and each run consists of 15000 tasks. It can be seen that Learn-Adapt performs consistently better than K-Adapt in all situations. 1), with more task allocation runs, the difference between Learn-Adapt and K-Adapt is gradually increasing. This is because when each agent has very few resources, agents have to allocate tasks to others. Thus, an effective network structure could reduce agents’ communication cost and management cost, and, further, raise the profit of the entire network.

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