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Tue, 30 Aug 2016 10:31:37 +0200
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Workshop on: Collective Adaptation in Very Large Scale Ubicomp: 
Towards a Superorganism of Wearables 

-----------------

Workshop at Ubicomp 2016: September 13, 2016

-----------------

SUMMARY AND THEME
The 3rd workshop (after the 2014 WS in Seattle and the 2015 WS in Osaka) 
asks questions  on the potential and opportunities  of turning massively 
deployed   wearable  systems  to   a  globe-spanning  superorganism   of 
socially  interactive  personal  digital  assistants.  While  individual 
wearables   are   of   heterogeneous   provenance   and   typically  act 
autonomously, it stands to reason that they can (and will) self-organize 
into  large scale  cooperative collectives,  with  humans  being  mostly 
out-of-the-loop.  A common objective  or central controller  may thereby 
not be assumed,  but rather volatile  network topologies,  co-dependence 
and internal competition,  non-linear  and non-continuous dynamics,  and 
sub-ideal,  failure-prone operation.  We refer to these emerging massive 
collectives  of  wearables  as a  "superorganism",  since  they  exhibit 
properties of a living organism (like e.g. 'collective intelligence') on 
their  own.  One  essential  aspect  of such  globe-spanning  collective 
ensembles is  that they often exhibit  properties typically observed  in 
complex systems,  like  (i) spontaneous,  dynamic network configuration, 
with  (ii) individual nodes acting in parallel,  (iii) constantly acting 
and reacting  to what  the other agents  are doing,  and  (iv) where the 
control tends  to be highly dispersed and decentralized.  If there is to 
be  any  coherent  behavior  in  the system,  it  (v) has to arise  from 
competition and cooperation  among  the individual nodes,  so  that  the 
overall  behavior  of  the system  is  the result  of  a huge number  of 
decisions made every moment by many individual entities. 

In order to properly exploit such superorganisms, this workshop concerns 
itself with the development  of a deeper scientific understanding of the 
foundational principles by which they operate. To this end, the workshop 
attempts to address the following foundational research concerns: 

- Understanding the trade-offs between the power of top-down (by design) 
adaptation means and bottom-up (by emergence) ones, also by studying how 
the two approaches co-exist in modern wearable ICT systems, and possibly 
contributing to smoothing the tension between the two approaches. 

- Understanding  the   "power  of  the  masses"   principle  as  far  as 
participatory wearable ICT processes are involved.  In particular,  this 
implies understanding how and to what extent even very simple collective 
phenomena and algorithms  - when involving billions of wearables  -  can 
express   forms  of  intelligence   much  superior  than  that  of  more 
traditional AI techniques. 

- Understanding  the issue  of  diversity  and  of diversity increase in 
complex systems  and  in  service/data  systems  and  how  diversity  of 
structure  and  behavior  is  currently  accommodated  in  wearable  ICT 
systems.  As of now, most studies focus on a limited number of different 
classes,  which is  far from  approximating  the  diversity of  existing 
systems. 

- Laying down new foundations for the modelling of large-scale Human-ICT 
organisms  and  their adaptive behaviors,  also  including lessons  from 
applied psychology, sociology, and social anthropology,  other than from 
systemic biology, ecology and complexity science. 

- Identifying models and tools by which individual organs of the systems 
can influence  and direct "by design"  the emergent adaptive behavior of 
the whole system, or at least of substantial parts of it. 

 
Further, the workshop attempts to address the following systems research 
concerns: 

- Opportunistic information collection:  Systems  need  to  be  able  to 
function in complex,  dynamic environments  where they have to deal with 
unpredictable  changes   in  available  infrastructures   and  learn  to 
cooperate with other systems and human beings  in complex self-organized 
ensembles. 
 	
- Collaborative Reasoning and Emergent Effects:  Reasoning  methods  and 
system models  are needed  that combine  machine learning  methods  with 
complexity theory to account for global emergent effects  resulting from 
feedback loops between  collaborative,  interconnected devices and their 
users. 

- Social Awareness:  Whereas today's  context-aware systems  are able to 
make  sense  of  the  activity  of  single  users  and  their  immediate 
environment,  future systems  should be able to analyze,  understand and 
predict  complex  social  phenomena  on  a broad range  of  spatial  and 
temporal scales. Examples of the derived information could be: shifts in 
collective opinions and social attitudes,  changes in consumer behavior, 
the emergence  of  tensions  in  communities,  demographics,  migration, 
mobility patterns, or health trends. 


PRELIMINARY AGENDA

8:30  Registration / Help desk opens
12:00 Lunch
13:30 Workshop Opening

14:00 Keynote - "Collective Addaptive Wearables - Technology
      Needs and Societal Breakthroughs"
      Franco Zambonelli
	  Universita di Modena e Reggio Emilia, Italy
	  
15:00 Paper Session 1
      Bernhard Anzengruber et al., "Understanding Individuals 
      in Masses: Case Study Vienna City Marathon"
	  
15:30 Coffee break

16:00 Paper Session 2
      Ali Farahani et al., "Self-* Properties in Collective
      Adaptive Systems"

      Mirko Viroli et al., "On Execution Platforms for
      Large-Scale Aggregate Computing"

      Bernhard Anzengruber et al., "AL17 - Triggering
      Interactions in an Ecosystem of Wearables"

      Andrea Omicini et al., "Challenges of Decentralized
      Coordination in Large-scale Ubicomp Systems"

18:00 Drafting of Workshop White Paper
19:00 Workshop closed


ORGANIZERS
- Alois Ferscha (University of Linz, Austria)
- Paul Lukowicz (DFKI, Germany)
- Franco Zambonelli (Universita di Modena e Reggio Emilia, Italy)


All Details and News: http://www.pervasive.jku.at/ubicomp16/

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