Recognition at end of Year 1

RECOGNITION year 1 review 10th November 2011

Cogni&ve contents

Franco Bagnoli and Andrea Guazzini University of Florence

1


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

2


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

3


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.

4


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.

5


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

6


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.

7


RECOGNITION approach

8

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 10th November 2011 9

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 10th November 2011 10

Tri-‐parGte model

Module I Unconscious knowledge perceptive and attentive processes

Relevance Heuristic

Module II Reasoning

Goal Heuristic Recognition Heuristic Solve Heuristic

Module III Learning

Evaluation Heuristic

Reac&on &me

Flexibility

Cogni&ve costs

External Data

Behavior


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)

11


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

12


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

13


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.

14


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).

15


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.

16


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.

17


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

18


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).

19

Technology

Recognition at end of Year 1