Editorial

Special Issue on Distributed Computing and Artificial Intelligence

Guesteditors

Juan M. CORCHADO, University of Salamanca, Spain

Li WEIGANG, University of Brasilia, Brazil

Javier BAJO, Polytechnic University of Madrid, Spain

Fei WU, Zhejiang University, China

Tiancheng LI, University of Salamanca, Spain

Contact Email: t.c.li@usal.es

4:1! Google’s artificial intelligence (AI) program,AlphaGo, has won Go Master Lee Sedol in a best-of-five competition held inKorean March 9-15, 2016. Seen by many as a landmark moment for AI, the outcomedid not come as a surprise, considering the excellent combination of 1920 CPUswith sophisticated AI algorithms, including neural networks and Monte Carlotree search (Silver et al. 2016; Gibney et al. 2016). Indeed, research ondistributed computing and artificial intelligence (DCAI) has matured during thelast decade and many effective applications are now deployed, performing anincreasingly important role in modern computer science, including the two mosthyped technologies: Internet of Things and Big Data. Indeed, it is fair tosay that the application of artificial intelligence in distributed environmentsis becoming an essential element of high added value and economic potential.

As a testimony to theconsiderable momentum in R&D activities regarding DCAI, this special issuecontains six outstanding papers from the 12th International Conference on DCAIand 13th International Conference on Practical Applications of Agents andMulti-Agent Systems held in the University of Salamanca, Spain, June 2015. The selectedcontributions cover new theories, techniques, and approaches on DCAI systems,from distributed adaptive searching to group decision-making, from multi-agentsystem for crisis management to multi-robot scheduling for flow shop, and fromambient intelligence for entertainment to multi-camera monitoring systems forrehabilitation therapy.

The first paper by Wall (2016) investigates theeffects of alternating the organizational dynamics/setting of distributedadaptive search (DAS) processes, with emphasis on the complexity ofinteractions between partial search problems assigned to search agents. DASprocesses occur in a large variety ofreal-world systems in which networked agents collaboratively search for higherlevels of performance, obtaining collective intelligence, for which one scientificchallenge is to coordinate and allow distributed agents to deal with thecross-agent interactions. The presented work is interesting because in order toreduce the problem complexity in many practical DAS applications, it isnecessary to segment the overall search problem into disjoint partial problems,and then delegate them to different search agents. In such cases, dependenciesacross the agents’ partial search problems become inevitable, which will causesignificant performance degradationif not properly taken into account.

The second paper by Carneiro et al. (2016) presents an intelligent negotiation model to supportthe group decision-making process, which facilitates arguments, complexalgorithms and agent modelling. In general, supporting group decision-making inubiquitous contexts is a complex task, as it has to deal with a large number offactors. Aware of the drawbacks of existing models that are barely adaptable tothe business world reality, the presented work makes use of a social networkinglogic which simultaneously preserves the amount and quality of intelligencegenerated in face-to-face meetings, while defining strategies to deal withimportant points such as the type of attributes in the multi-criteria problems,agents reasoning and intelligent dialogues.

The third paper by Britoy et al. (2016) presents the use of Situated Artificial Institution(SAI) within a hybrid, interactive, normative multi-agent system to regulatehuman collaboration in crisis management. To provide a context aware crisisregulation, this paper introduces a constitutive level between environmentaland normative states, providing a loose coupling of normative regulation withenvironment evolution, thus making it possible to keep both levels independentand easy-to-change in the face of complex and changing crisis situations. Normsare specified to regulate the actions of human actors based on both statusfunctions and the actors’ actions, leading to a declarative and distinct SAImodelling that manages the crisis with a context-aware crisis regulation.

The forth paper by Rincon et al. (2016) involves a human-agent society where virtual agentsand humans coexist and interact transparently into a fully integratedenvironment. This paper presents an ambient intelligence application wherehumans are immersed into a system and are treated as agents with an emotionalstate. What is particularly interesting is the resulting social emotional modelwhich is able to maximize the welfare of humans by always playing the mostappropriate music, in which each individual will be represented by an agentthat has an emotional response according to its musical taste. The varyingemotions of each agent are then collected and used to update the social emotionof the group. The emotional state of agents is indicated as one important issueto consider in the human-agent society.

The fifth paper by Nielsen et al. (2016) presents a constraint propagation driven approach formulti-robot task allocation in flow shop scheduling, providing a prompt serviceto a set of routine direct and reverse queries. This involves aresource-constrained multi-product scheduling problem for an AGV- served flowshop, where multiple material handling transport modes provide movement of workpieces between machining centers in the Multimodal Transportation Network(MTN). This network of repetitively acting local transportation modesencompassing an MTN structure provides a framework for multimodal processscheduling treated in terms of optimizing AGV fleet scheduling problems,subject to fuzzy operation time constraints and uncertainty of robots. In thepresented work, both production takt and operations execution time aredescribed by imprecise data.

In contrast to the above theoretical and/or novelapproach study, the last paper by Oliver etal. (2016) stands out more from an experimental perspective, whichinvestigates in detail how the overlap of several infrared beams affects thetracked position of the user, depending on the angle of incidence of light,distance to the target, distance between sensors, and the number of capturedevices used. The experiment is carried out based on the Kinect camera which probablyrepresents one of the latest advances in cameras and three-dimensionalcapturing technology. The experimental findings have enlightening significancefor the design of intelligent patient-rehabilitation environments. Thisindicates a good R&D direction for utilizing AI technologies to improvepeople’s lives.

We would like to thank all theauthors for their contribution to this special issue. We appreciate thededication from the reviewers for their time and detailed reviews.The great support from the editorialoffice is also highly appreciated. It isour hope that these papers capture some of the latest major scientificdevelopments and that they can serve as a springboard for further improvementsand developments.

References

Carneiro,J., Martinho, D., Marreiros, G., et al., 2016. Intelligent negotiation modelfor ubiquitous group decision scenarios. Front. Inform. Technol. Electron.Eng., 17(4):

296-308.http://dx.doi.org/10.1631/FITEE.1500344

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

http://dx.doi.org/10.1631/FITEE.1500369

Gibney,E., 2016. Google AI algorithm masters ancient game of Go. Nature, 529:445-446. http://dx.doi.org/10.1038/529445a

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

http://dx.doi.org/10.1631/FITEE.1500359

Oliver,M., Montero, F., Molina, J.P., et al., 2016. Multicamera systems forrehabilitation therapies: a study of the precision of Microsoft Kinect sensors.Front. Inform. Technol. Electron. Eng., 17(4):348-364.

http://dx.doi.org/10.1631/FITEE.1500347

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

http://dx.doi.org/10.1631/FITEE.1500343

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

http://dx.doi.org/10.1038/nature16961

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.

http://dx.doi.org/10.1631/FITEE.1500306

All the papers can be accessed @  Frontiers of Information Technology & Electronic Engineering