By Michal Pěchouček (auth.), Yves Demazeau, Frank Dignum, Juan M. Corchado, Javier Bajo Pérez (eds.)
PAAMS, the overseas convention on functional functions of brokers and Multi-Agent structures is a global each year degree to offer, to debate, and to disseminate the newest advances and an important results with regards to real-world functions. It offers a distinct chance to carry multi-disciplinary specialists, lecturers and practitioners jointly to switch their adventure within the improvement of brokers and Multi-Agent platforms. This quantity provides the papers which have been accredited for the 2010 version. those articles catch the main cutting edge effects and this year’s advances. every one paper has been reviewed by means of 3 various reviewers, from a global com-mittee composed of eighty two participants from 26 diverse nations. From the sixty six submissions bought, 19 have been chosen for complete presentation on the convention, and 14 have been authorised as brief papers. additionally, PAAMS'10 included unique classes and workshops to counterpoint the ordinary application, which incorporated eighty five authorized papers.
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Extra info for Advances in Practical Applications of Agents and Multiagent Systems: 8th International Conference on Practical Applications of Agents and Multiagent Systems (PAAMS 2010)
S´anchez-Anguix et al. However, some problems arise in most tourism recommender systems. First, each service provider is responsible for keeping up-to-date their business information in the recommender system. If it is not carried out by an automatic process, keeping information up-to-date is a costly task. Second, there may be third parties (tourist service providers that are not part of the system) which offer types of services that are not offered in system. The inclusion of information of third party services available on the Web may enhance recommendations given to tourists, and therefore improve the system.
When the GPU manager thread is signaled by the AE Threads, it copies the object positions array into the GPU and launches the collision tests. When the collision tests finish, the GPU manager thread copies the results from the GPU memory into the SDB module. Then, it signals the AE threads to start replying to CPs or ASs. Fig. 2 Internal structure of an action server using a GPU The object positions array has NOBJECT S elements, that is, as many elements as agents managed by a server. An agent identifier is associated to each request, in order to update the proper array position.
From that point up, the same behavior is shown when no GPU is used. However, when using GPU the plot shows a flat slope. Beyond a given threshold, the average response time is not increased because all the collisions tests are performed in parallel in the GPU. Additionally, Figure 3 b) shows that the CPU-based crowd simulation can support up to 3500 agents while providing average response times below the 250 milliseconds threshold. When GPU is used, the number of agents supported grows up to 5300 agents, providing an improvement of a 50%.