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Recognition at end of Year 1

Recognition at end of Year 1



Presentation by Recognition at the Awareness Inter-Project Day, Bologna 2012

Presentation by Recognition at the Awareness Inter-Project Day, Bologna 2012



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    Recognition at end of Year 1 Recognition at end of Year 1 Presentation Transcript

    • Cogni&ve  contents  Franco  Bagnoli  and  Andrea  Guazzini   University  of  Florence   RECOGNITION  year  1  review   1   10th  November  2011  
    • MoGvaGon  and  Background  •  Pervasive  compuGng  devices   –  Mobility,  Portability   –  Wireless  connecGvity   –  Sensors   –  MulGmedia  capabiliGes  •  Cheap  and  portable  hardware  with  processing,  storage   and  communicaGon  capability   –  FacilitaGng  new  ways  to  provide  and  share  content   –  CreaGng  more  and  more  diverse  content       RECOGNITION  year  1  review   2   10th  November  2011  
    • Content-­‐centric  approach  •  Content  is  generated  everywhere   –  IntegraGon  human  acGvity  and  mobility   –  Greater  user  parGcipaGon  (e.g.,  web  2.0)    •  Content  is  diverse   –  Pictures,  data  from  sensors,  news,  caching   from  the  Internet,  messages   –  Unleashed  from  tradiGonal  Internet  •  Content  can  be  shared  &  forwarded   –  Short  range  wireless  technology  for   forwarding  and  sharing   –  Awareness  of  locaGon  and  context  –  a   spaGal  context     RECOGNITION  year  1  review   3   10th  November  2011  
    • RECOGNITION  mission  •  Seeking  to  capture  the  behavioural  characterisGcs  of  the   most  intelligent  living  species,  namely  human  beings  •  Fundamental  approaches  to  cogniGon  that  are  grounded  in   the  organ  responsible  for  the  most  sophisGcated  autonomic   behaviour  –  the  brain…  •  PotenGally  begin  to  represent  the  needs  and  characterisGcs   of  the  individual  users  inside  the  network  itself  and  inside   content.    •  Include  fundamental  characterisGcs  of  human  cogniGve   behaviour,  such  as  the  ability  to  infer,  believe,  understand,   and  assert  relevance,  interact  and  respond  in  the  face  of   massive  amounts  of  informa&on.   RECOGNITION  year  1  review   4   10th  November  2011  
    • The  Approach…  •  Developing  models  of  cogni&ve  behaviour  from  psychology   that  are  transferable  to  the  ICT  domain;   –  Key  psychological  principles  to  facilitate  self-­‐awareness  •  ExploiGng  models  of  cogniGve  behaviour  for  a  content-­‐centric   Internet   –  self-­‐awareness  can  provide  new  levels  of  cogniGve   behaviour  to  enhance  content  acquisiGon.   RECOGNITION  year  1  review   5   10th  November  2011  
    • Human  Awareness  Behaviours    •  Approach:  Capture  &  exploit  key  behaviours  of   the  most  intelligent  living  species   –  Human  capability  is  phenomenal  in   navigaGng  complex  &  diverse  sGmuli   –  Filter  &  suppress  informaGon  in  “noisy”   situaGons  with  ambient  sGmuli   –  Extract  knowledge  in  presence  of   uncertainty   –  Exercise  rapid  value  judgment  for   prioriGsaGon   –  Engage  a  social  context  and  mulG-­‐scale   learning   RECOGNITION  year  1  review   6   10th  November  2011  
    • Project  ObjecGves  1.  To  iden&fy  and  engage  a  robust  psychological  basis  for  self-­‐ awareness  in  ICT.     –  This  will  involve  engaging  cogniGve-­‐based  processes  from  the   human  brain  that  enable  understanding,  inference  and   relevance  to  be  established  while  suppressing  irrelevant   informaGon  in  the  context  of  massive  scale  and  heterogeneity.  2.  To  exploit  the  psychological  basis  for  self-­‐awareness  in  a  content   centric  Internet.   •  This  will  involve  engaging  the  spaGal  dimension,   interacGons  and  intelligent  processes  that  reflect  cogniGve   behavioural  heurisGcs  to  provide  content  and  knowledge   flow  to  other  parGcipants  and  network  components.   RECOGNITION  year  1  review   7   10th  November  2011  
    • RECOGNITION  approach    CogniGve  psychological  basis   For  awareness  and  understanding     Defining  key  principles  for  exploitaGon  by   technology  components       Embedding  these  principles  for     self-­‐awareness  in  autonomic  content   acquisiGon  in  pervasive    environments  PotenGal  change  in  behaviour  due  to   self–awareness  in  ICT   RECOGNITION  year  1  review   8   10th  November  2011  
    • Minimal  self-­‐awareness  cogniGve  agent  Self-­‐awareness   can   be   classified   on   the   basis   of   three   criteria:   Gmescales,   cogniGve  costs  and  evoluGonary  features.   Timescales  -­‐(Reac&on  &mes)   •  Unconscious  Knowledge  (PercepGon  and  Pre-­‐ahenGve  acGvaGons)-­‐>  Fast  (<.500  ms)   •  Conscious  knowledge  (reasoning)  -­‐>  medium  (from  seconds  to  hours)   •  Learning/development  -­‐>  slow  (from  minutes  to  month)   Cost  (Cogni&ve  Economy  Principle  -­‐  Amount  of  neural  ac&va&on)   •  Unconscious  knowledge  -­‐>  light  (small  and  local  acGvaGons)     •  Conscious  knowledge    -­‐>  heavy  (large  and  diffused  acGvaGons)   •   Learning/development  -­‐>  very  heavy  (diffused  acGvaGons)   Evolu&onary  features  (Cogni&ve  development)   •  Unconscious  knowledge  -­‐>  criGcal  period  and   Hebbian  learning  only  (ACTr)     •  Conscious  knowledge  -­‐>  trial  and  error,  observaGon/imitaGon  and  inducGon.   •  Learning/development  -­‐>  fixed  hard  wired  rules.   RECOGNITION  year  1  review   9   10th  November  2011  
    • External   Tri-­‐parGte  model    Data   Reac&on  &me   Module I Unconscious knowledge Flexibility   perceptive and attentive processes Relevance Heuristic Cogni&ve  costs   Module II Reasoning Goal Heuristic Recognition Heuristic Solve Heuristic Module III Learning Behavior Evaluation Heuristic RECOGNITION  year  1  review   10   10th  November  2011  
    • An  applicaGon:     cogniGve  audio  stream  •  Many  people  live  inside  an  audio  sphere:  portable  music,  radio,   ambient  music..  •  Music  streams  (playlists)  can  be  assembled  manually,  or  by  means   of  automaGc  systems:   –  Randomly  (shuffling)   –  Based  on  similariGes  among  clips  (Pandora)   –  SimilariGes  among  users  (like  amazon)   –  Based  on  mood  (moodagent)     –  SubscripGon  (podcasts)   –  DelegaGon  (radio)   –  Direct  suggesGon  (friends)   RECOGNITION  year  1  review   11   10th  November  2011  
    • The  “radio”  structure  •  The  delegaGon  mode  (i.e.,  classical  radio)  allows  the  discovering  of   new  elements  (informaGon,  entertainment,  new  genres)  •  Favours  social  interacGon  (commenGng,  voGng)  and  parGcipaGon  •  But  is  hard  to  be  personalized   RECOGNITION  year  1  review   12   10th  November  2011  
    • CogniGve  playlist  •  Context:  locaGon,  Gme,  weekday,  status  (e.g.,  work,  commuGng,   home..),  network  access/bandwidth,  mood  (user  input),  memory   (played  clips),  feedback  (user  input),  user  profile  •  External  data:  sugges&ons  from  a  server,  based  on  user  pahern   similariGes,  clip  similariGes,  user  choices,  direct  suggesGons  from   social  networks/friends     RECOGNITION  year  1  review   13   10th  November  2011  
    • SuggesGons  •  SuggesGons  contains  the  descripGon  of  the  resource  and  its   availability  (downloadable,  local,  stream,  permission,  cost),  clip   characterisGcs  that  can  be  used  for  context  matching.    •  They  originate  the  actual  playlist  according  with  their  score,   assigned  by  methods  (schemes).  •  A  dynamical  score  is  assigned  to  suggesGons  by  schemes  (actually,   each  scheme  proposes  a  score).  The  score  is  recalculated   dynamically  since  the  context  and  the  schemes  may  vary.   RECOGNITION  year  1  review   14   10th  November  2011  
    • From  suggesGons  to  playlist  •  The  goal  is  that  of  building  a  dynamical  playlist  based  by  the  match   (score)  between  suggesGons  and  the  context.  •  The  matching  is  performed  by  methods  (schemes)  that  compete/ collaborate  for  assigning  scores  to  suggesGons.  For  instance,  a   method  may  propose  random  scores  (shuffling),  simply  avoiding   repeGGons,  another  may  propose  scores  based  on  status  and  clip   genre.  •  Schemes  themselves  have  a  score,  assigned  to  heurisGcs  (meta-­‐ schemes),  according  to  user  feedback  (for  instance  clip  skipping,   voGng,  suggesGons).     RECOGNITION  year  1  review   15   10th  November  2011  
    • HeurisGcs  •  HeurisGcs  are  similar  to  schemes,  and  assign  a  score  to  schemes,   based  on  feedbacks,  performances  of  schemes,  collisions.  •  For  instance,  it  may  happen  that  no  schemes  proposes  a  sufficiently   high  score  to  any  suggesGon  in  a  given  context  (this  is  reported  to   the  server),  then  heurisGcs  may  decide  to  import  other  schemes   from  the  server  •  It  may  happen  also  that  a  scheme  systemaGcally  proposes  scores   that  are  different  from  others,  or  finally  that  the  clips  selected  by  a   method  receives  negaGve  feedbacks.    The  method  can  be  purged  by   the  pool.   RECOGNITION  year  1  review   16   10th  November  2011  
    • The  compeGGve  environment  •  HeurisGcs  try  to  maintain  an  assorted  pool  of  schemes  that   cooperates  (proposing  scores  that  are  not  systemaGcally  in  conflict)   and  that  do  not  receive  negaGve  feedbacks.    •  The  scores  are  used  to  instanGate  suggesGons  into  a  short  playlist   (since  context  changes),  and  possibly  also  to  build  a  tree   anGcipaGng  context  changes  (for  instance,  switching  from   commuGng  to  work)    •  The  feedback  (for  instance  that  a  clip  has  been  listened  or  skipped   or  that  a  suggesGon  is  never  promoted  to  playlist)  is  reported  to  the   server,  together  withe  direct  suggesGons  to  friends.     RECOGNITION  year  1  review   17   10th  November  2011  
    • The  server  architecture  •  The  server  is  essenGally  a  database  of  user  profiles  and  clip  choices  •  From  the  overlap  among  user  profiles  (clip  choices,  messages,  social   informaGon)  one  obtains  the  affinity  among  users,  that  can  be  used   to  infer  suggesGons  based  on  heurisGcs  (weighted,  take  the  best,   etc.)    •  It  may  use  also  databases  of  clip  similariGes  like  pandora  •  Collects  direct  suggesGons   RECOGNITION  year  1  review   18   10th  November  2011  
    • Conclusions  •  Three-­‐level  cogniGve  system  (server/suggesGons,  schemes,   heurisGcs)  •  Related  to  Hypermusic  (context-­‐based,  user  input)  •  Ecosystem-­‐like,  compeGGon/cooperaGon  •  Decentralized,  adapGve,  pervasive  •  Can  be  exported  to  other  scenarios  (e.g.,  learning  objects).     RECOGNITION  year  1  review   19   10th  November  2011