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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Tian-cheng Li

http://orcid.org/0000-0002-0499-5135

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.4 P.281-282

http://doi.org/10.1631/FITEE.DCAI2015


Special issue on distributed computing and artificial intelligence


Author(s):  Juan M. Corchado, Li Weigang, Javier Bajo, Fei Wu, Tian-cheng Li

Affiliation(s):  University of Salamanca, Spain; more

Corresponding email(s):   t.c.li@usal.es

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Juan M. Corchado, Li Weigang, Javier Bajo, Fei Wu, Tian-cheng Li. Special issue on distributed computing and artificial intelligence[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(4): 281-282.

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Abstract: 
4:1! Google’s artificial intelligence (AI) pro-gram, AlphaGo, has won Go Master Lee Sedol in a best-of-five competition held in Korean March 9−15, 2016. Seen by many as a landmark moment for AI, the outcome did not come as a surprise, considering the excellent combination of 1920 CPUs with so-phisticated AI algorithms, including neural networks and Monte Carlo tree search (Gibney, 2016; Silver et al., 2016). Indeed, research on distributed computing and artificial intelligence (DCAI) has matured during the last decade and many effective applications are now deployed, performing an increasingly important role in modern computer science, including the two most hyped technologies: Internet of Things and Big Data. Indeed, it is fair to say that the application of artificial intelligence in distributed environments is becoming an essential element of high added value and economic potential.
  As a testimony to the considerable momentum in R&D activities regarding DCAI, this special issue contains six outstanding papers from the 12th International Conference on DCAI and 13th International Conference on Practical Applications of Agents and Multi-Agent Systems held in the University of Sal-amanca, Spain, June 2015. The selected contributions cover new theories, techniques, and approaches on DCAI systems, from distributed adaptive searching to group decision making, from multi-agent system for crisis management to multi-robot scheduling for flow shop, and from ambient intelligence for entertainment to multi-camera monitoring systems for rehabilitation therapy.

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Reference

[1]Carneiro, J., Martinho, D., Marreiros, G., et al., 2016. Intelligent negotiation model for ubiquitous group decision scenarios. Front. Inform. Technol. Electron. Eng., 17(4): 296-308.

[2]de Brito, M., Thévin, L., Garbay, C., et al., 2016. Supporting flexible regulation of crisis management by means of situated artificial institution. Front. Inform. Technol. Electron. Eng., 17(4):309-324.

[3]Gibney, E., 2016. Google AI algorithm masters ancient game of Go. Nature, 529:445-446.

[4]Nielsen, I., Wójcik, R., Bocewicz, G., et al., 2016. Multimodal processes optimization subject to fuzzy operation time constraints: declarative modeling approach. Front. Inform. Technol. Electron. Eng., 17(4):338-347.

[5]Oliver, M., Montero, F., Molina, J.P., et al., 2016. Multi- camera systems for rehabilitation therapies: a study of the precision of Microsoft Kinect sensors. Front. Inform. Technol. Electron. Eng., 17(4):348-364.

[6]Rincon, J.A., Bajo, J., Fernandez, A., et al., 2016. Using emotions for the development of human-agent societies. Front. Inform. Technol. Electron. Eng., 17(4):325-337.

[7]Silver, D., Huang, A., Maddison, C.J., et al., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529:484-489.

[8]Wall, F., 2016. Organizational dynamics in adaptive distributed search processes: effects on performance and the role of complexity. Front. Inform. Technol. Electron. Eng., 17(4):283-295.

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