SlideShare uma empresa Scribd logo
1 de 19
Implementation of DEvelopmentAl Learning (IDEAL) [email_address] http://liris.cnrs.fr/ideal ANR-RPDOC 2010 8 octobre 2010 Implémentation de mécanismes de développement cognitif précoce  dans des agents artificiels autonomes
Plan de la pésentation ,[object Object],[object Object],[object Object],[object Object],[object Object],5/20/2010
Développement cognitif précoce ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],5/20/2010
Organisation autonome des comportements ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],5/20/2010
Ceci n'est pas un labirynthe …  C'est un environnement offrant des  régularités séquentielles hiérarchiques 5/20/2010
Ceci n'est pas un "buffer perceptif" Touch: Move: Turn: 0 -1 0 10 -10 0 0 0 -5 …  ce sont des schemes sensorimoteurs (Piaget, 1937) 5/20/2010
Ceci n'est pas un mécanisme de récompense ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],5/20/2010
Légende 3D 5/20/2010
Démonstration Link Touch: Bump: Ouch! Oh! Surprise: Rub: 5/20/2010
Mécanisme d'apprentissage Turn, w Touch, w Turn S (0) Touch S (-1) Touch F (0) Schema Act Schema's context Schema's intention Act's schema Learning Move, w Move S (10) Bump F (-10) Touch-Move, w Touch-Move S (10) Touch-Move F(-1) Turn F (-5) 5/20/2010 Touch-Move-Turn, w Touch-Move-Turn S (10)
Trace   O     O  O            O  O O  O   O   O O  O        O   O O O   O     O    O  O    O  O  (  O )  O  O O((  O )  ) (  O )  O  O O  (  O )  O  O  ( O  )(O((  O )  ))    O  O  O  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50  51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 68 69 70 71 72 73 74 76 77 78 79 80 O 67 O 75 81 82 83 84 85 86 87 88 89 90 O  ( O  )(O((  O )  )) ( O  )(O((  O )  )) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) O   O  O  O  O  O   O  O (  O )  (  O )   O (  O )  O  (  O )   O O((  O )  ) O((  O )  ) O((  O )  ) O O((  O )  ) ( O  )(O((  O )  )) ( O  )(O((  O )  )) ( O  )(O((  O )  )) ( O  )(O((  O )  )) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) O((  O )  ) ( O  )(O((  O )  )) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) O    X X Touch Forward Right Left Succeed Fail (  O )  O((  O )  ) ( O  )(O((  O )  )) ((( O  )(O((  O )  ))) (( O  )(O((  O )  )))) ((( O  )(O((  O )  ))) (( O  )(O((  O )  )))) ( O  )(O((  O )  )) 91 (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) Control cycles S4 [S4,F] S5 [S5,S] S7 S6 [S6,F] S8 S7 S10 S8 S12 S10 [S2,S] 5/20/2010
Apprentissage du context S7 S3, S S7,S Time S8 S10, 4 S8, S (3) S10,S S9, 6 S9,S S13,1 Current situation S6,S (5) Base situation S6 S3,S 83 84 S12,1 S11 S11,S Enacted schema Enacted act S5, S S2, S 5/20/2010
Résultats ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],5/20/2010
Spécificités ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],5/20/2010
Représentation alternative de la cognition Scheme Scheme Symbolic computation Perception Action Environment Time Scheme Scheme Scheme Préserve l'unité perception/action (de nombreux auteurs) Ancre le sens dans l'activité (Harnad, 1990) Ouvre la voie vers d'autre mécanismes (Piaget, 1937) Elaboration 5/20/2010 De: Vers:
Faiblesses ,[object Object],[object Object],[object Object],[object Object],5/20/2010
Challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],5/20/2010
Projet IDEAL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],5/20/2010
Déroulement ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],5/20/2010

Mais conteúdo relacionado

Destaque

Social Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie InsightsSocial Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie Insights
Kurio // The Social Media Age(ncy)
 

Destaque (20)

AI Trends in Creative Operations 2024 by Artwork Flow.pdf
AI Trends in Creative Operations 2024 by Artwork Flow.pdfAI Trends in Creative Operations 2024 by Artwork Flow.pdf
AI Trends in Creative Operations 2024 by Artwork Flow.pdf
 
Skeleton Culture Code
Skeleton Culture CodeSkeleton Culture Code
Skeleton Culture Code
 
PEPSICO Presentation to CAGNY Conference Feb 2024
PEPSICO Presentation to CAGNY Conference Feb 2024PEPSICO Presentation to CAGNY Conference Feb 2024
PEPSICO Presentation to CAGNY Conference Feb 2024
 
Content Methodology: A Best Practices Report (Webinar)
Content Methodology: A Best Practices Report (Webinar)Content Methodology: A Best Practices Report (Webinar)
Content Methodology: A Best Practices Report (Webinar)
 
How to Prepare For a Successful Job Search for 2024
How to Prepare For a Successful Job Search for 2024How to Prepare For a Successful Job Search for 2024
How to Prepare For a Successful Job Search for 2024
 
Social Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie InsightsSocial Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie Insights
 
Trends In Paid Search: Navigating The Digital Landscape In 2024
Trends In Paid Search: Navigating The Digital Landscape In 2024Trends In Paid Search: Navigating The Digital Landscape In 2024
Trends In Paid Search: Navigating The Digital Landscape In 2024
 
5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summary5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summary
 
ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd
 
Getting into the tech field. what next
Getting into the tech field. what next Getting into the tech field. what next
Getting into the tech field. what next
 
Google's Just Not That Into You: Understanding Core Updates & Search Intent
Google's Just Not That Into You: Understanding Core Updates & Search IntentGoogle's Just Not That Into You: Understanding Core Updates & Search Intent
Google's Just Not That Into You: Understanding Core Updates & Search Intent
 
How to have difficult conversations
How to have difficult conversations How to have difficult conversations
How to have difficult conversations
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data Science
 
Time Management & Productivity - Best Practices
Time Management & Productivity -  Best PracticesTime Management & Productivity -  Best Practices
Time Management & Productivity - Best Practices
 
The six step guide to practical project management
The six step guide to practical project managementThe six step guide to practical project management
The six step guide to practical project management
 
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
 
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...
 
12 Ways to Increase Your Influence at Work
12 Ways to Increase Your Influence at Work12 Ways to Increase Your Influence at Work
12 Ways to Increase Your Influence at Work
 
ChatGPT webinar slides
ChatGPT webinar slidesChatGPT webinar slides
ChatGPT webinar slides
 
More than Just Lines on a Map: Best Practices for U.S Bike Routes
More than Just Lines on a Map: Best Practices for U.S Bike RoutesMore than Just Lines on a Map: Best Practices for U.S Bike Routes
More than Just Lines on a Map: Best Practices for U.S Bike Routes
 

Implémentation de mécanismes de développement cognitif précoce dans des agents artificiels autonomes

  • 1. Implementation of DEvelopmentAl Learning (IDEAL) [email_address] http://liris.cnrs.fr/ideal ANR-RPDOC 2010 8 octobre 2010 Implémentation de mécanismes de développement cognitif précoce dans des agents artificiels autonomes
  • 2.
  • 3.
  • 4.
  • 5. Ceci n'est pas un labirynthe … C'est un environnement offrant des régularités séquentielles hiérarchiques 5/20/2010
  • 6. Ceci n'est pas un "buffer perceptif" Touch: Move: Turn: 0 -1 0 10 -10 0 0 0 -5 … ce sont des schemes sensorimoteurs (Piaget, 1937) 5/20/2010
  • 7.
  • 9. Démonstration Link Touch: Bump: Ouch! Oh! Surprise: Rub: 5/20/2010
  • 10. Mécanisme d'apprentissage Turn, w Touch, w Turn S (0) Touch S (-1) Touch F (0) Schema Act Schema's context Schema's intention Act's schema Learning Move, w Move S (10) Bump F (-10) Touch-Move, w Touch-Move S (10) Touch-Move F(-1) Turn F (-5) 5/20/2010 Touch-Move-Turn, w Touch-Move-Turn S (10)
  • 11. Trace   O     O  O            O  O O  O   O   O O  O        O   O O O   O     O    O  O    O  O  (  O )  O  O O((  O )  ) (  O )  O  O O  (  O )  O  O  ( O  )(O((  O )  ))    O  O  O  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50  51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 68 69 70 71 72 73 74 76 77 78 79 80 O 67 O 75 81 82 83 84 85 86 87 88 89 90 O  ( O  )(O((  O )  )) ( O  )(O((  O )  )) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) O   O  O  O  O  O   O  O (  O )  (  O )   O (  O )  O  (  O )   O O((  O )  ) O((  O )  ) O((  O )  ) O O((  O )  ) ( O  )(O((  O )  )) ( O  )(O((  O )  )) ( O  )(O((  O )  )) ( O  )(O((  O )  )) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) O((  O )  ) ( O  )(O((  O )  )) (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) O    X X Touch Forward Right Left Succeed Fail (  O )  O((  O )  ) ( O  )(O((  O )  )) ((( O  )(O((  O )  ))) (( O  )(O((  O )  )))) ((( O  )(O((  O )  ))) (( O  )(O((  O )  )))) ( O  )(O((  O )  )) 91 (( O  )(O((  O )  ))) (( O  )(O((  O )  ))) Control cycles S4 [S4,F] S5 [S5,S] S7 S6 [S6,F] S8 S7 S10 S8 S12 S10 [S2,S] 5/20/2010
  • 12. Apprentissage du context S7 S3, S S7,S Time S8 S10, 4 S8, S (3) S10,S S9, 6 S9,S S13,1 Current situation S6,S (5) Base situation S6 S3,S 83 84 S12,1 S11 S11,S Enacted schema Enacted act S5, S S2, S 5/20/2010
  • 13.
  • 14.
  • 15. Représentation alternative de la cognition Scheme Scheme Symbolic computation Perception Action Environment Time Scheme Scheme Scheme Préserve l'unité perception/action (de nombreux auteurs) Ancre le sens dans l'activité (Harnad, 1990) Ouvre la voie vers d'autre mécanismes (Piaget, 1937) Elaboration 5/20/2010 De: Vers:
  • 16.
  • 17.
  • 18.
  • 19.

Notas do Editor

  1. I will first define what I mean by hierarchical behavior and give some examples Then the presentation will have two parts: the first half will be about analyst-driven hierarchical behavior learning. The second half will be about self-driven hierarchical behavior learning. The first par is situated in the framework of knowledge discovery from database. That is how an analyst learns something from data describing the activity of a subject. I will present a system that we have implemented for trace analysis I will give example analysis Then I will discuss how we can make this learning mechanism autonomous. It is situated in the domain of developmental learning. I will present an algorithm for self-motivated hierarchical sequence learning.