Agents and Data Mining Interaction: 4th International by Ana L. C. Bazzan (auth.), Longbing Cao, Vladimir Gorodetsky,
By Ana L. C. Bazzan (auth.), Longbing Cao, Vladimir Gorodetsky, Jiming Liu, Gerhard Weiss, Philip S. Yu (eds.)
This ebook constitutes the completely refereed post-conference complaints of the 4th overseas Workshop on brokers and knowledge Mining interplay, ADMI 2009, held in Budapest, Hungary in may perhaps 10-15, 2009 as an linked occasion of AAMAS 2009, the eighth foreign Joint convention on self sufficient brokers and Multiagent Systems.
The 12 revised papers and a couple of invited talks awarded have been rigorously reviewed and chosen from a variety of submissions. equipped in topical sections on agent-driven information mining, facts mining pushed brokers, and agent mining functions, the papers exhibit the exploiting of agent-driven facts mining and the resolving of serious information mining difficulties in idea and perform; the best way to increase information mining-driven brokers, and the way info mining can advance agent intelligence in learn and useful functions. topics which are additionally addressed are exploring the mixing of brokers and information mining in the direction of a super-intelligent info processing and structures, and deciding upon demanding situations and instructions for destiny examine at the synergy among brokers and information mining.
Read Online or Download Agents and Data Mining Interaction: 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers PDF
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Additional resources for Agents and Data Mining Interaction: 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers
MAE = ∑ki=1 |pi − r| k i = [1, 2, . . , k] , (8) where k - the number of records used for testing; pi - real value of the key parameter for record di ; r - the value of the key parameter forecasted by the system. Logical error provides information about the logical potential of the system. To calculate the logical error, it is necessary to define logically correct and logically incorrect decisions. As applied to the task of forecasting product life cycle phase transition period, logically correct and logically incorrect decisions could be described as follows: 1.
The third possible case (C3) is that the BMC contains several possible transition points, but there is no one with an expressed appearance frequency present. For this case several solutions are possible: (a) Solution S3. The Decision Analysis Agent asks the Data Mining Agent if the neural network ni where the BMC for a current product was found, is the only one network in the Neural Block or if it is the network that processes time series with maximal possible duration. If so, then the Decision Analysis Agent follows the next two rules: i.
Production planning is the main aspect for a manufacturer affecting an income of a company. Correct production planning policy, chosen for the right product at the right moment in the product life cycle (PLC), lessens production, storing and other related costs. This arises such problems to be solved as defining the present a PLC phase of a product as also determining a transition point - a moment of time (period), when the PLC phase is changed. The paper presents the Agents Based Data Mining and Decision Support system, meant for supporting a production manager in his/her production planning decisions.