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Agentes Pedagogicos

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In Portuguese. …

In Portuguese.
Apresentação sobre agentes pedagógicos já um pouco antiga.

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  • 1. Joana Paulo, nº 44048 Joana.Paulo@RNL.IST.UTL.Pt 1 © Joana Lúcio Paulo
  • 2. Agentes Pedagógicos: Sumário •  O que são agentes Pedagógicos? •  Características de um Agente Pedagógico •  Exemplos de Agentes Pedagógicos •  Trabalho Futuro •  Considerações Finais •  Referências 2 © Joana Lúcio Paulo
  • 3. Agentes Pedagógicos: Sumário •  O que são agentes Pedagógicos? •  Características de um Agente Pedagógico •  Exemplos de Agentes Pedagógicos •  Trabalho Futuro •  Considerações Finais •  Referências 3 © Joana Lúcio Paulo
  • 4. O que são Agentes Pedagógicos? Agente? Pedagógico? 4 © Joana Lúcio Paulo
  • 5. O que são Agentes Pedagógicos? •  O que é um agente? Segundo Russell e Norvig, considera-se um agente, tudo aquilo que pode percepcionar o ambiente em que se encontra através de sensores e que responde actuando nesse ambiente por meio de actuadores. 5 © Joana Lúcio Paulo
  • 6. Os Agentes Pedagógicos AGENTES Biológicos Robóticos Computacionais de Software Vida Artificial Tarefas Específicas de Entretenimento Pedagógicos 6 © Joana Lúcio Paulo
  • 7. O que são Agentes Pedagógicos? •  O que é a Pedagogia? substantivo feminino, do grego paidagogía, teoria da arte, filosofia ou ciência da educação, com vista à definição dos seus fins e dos meios capazes de os realizar
 7 © Joana Lúcio Paulo
  • 8. A Pedagogia implica •  Alguém que saiba ensinar e/ou acompanhar •  Alguém que queira aprender 8 © Joana Lúcio Paulo
  • 9. O que são Agentes Pedagógicos? Agentes Pedagógicos são agentes autónomos que apoiam a aprendizagem humana, interactuando com os alunos em ambientes de aprendizagem interactivos. 9 © Joana Lúcio Paulo
  • 10. Agentes Pedagógicos: Sumário •  O que são agentes Pedagógicos? •  Características de um Agente Pedagógico •  Exemplos de Agentes Pedagógicos •  Trabalho Futuro •  Considerações Finais •  Referências 10 © Joana Lúcio Paulo
  • 11. Características de um Agente Pedagógico •  Aquilo que é desejável é um agente com: –  Robustez em ambientes ricos e imprevisíveis –  Coordenação do comportamento próprio com os comportamentos dos outros agentes –  Coerência no comportamento escolhido como reacção a um estímulo –  Capacidade de arbítrio entre acções alternativas –  Responder a múltiplos estímulos do ambiente 11 © Joana Lúcio Paulo
  • 12. Mais Características . . . –  Adaptar-se às necessidades dos estudantes e ao estado actual do ambiente de aprendizagem –  Fornecer feedback contínuo aos estudantes durante seu trabalho: •  Oferecer ajuda quando necessário •  Dar explicações que clarifiquem •  Responder a perguntas dos alunos –  Aparentar características naturais para os estudantes, e induzi-los às mesmas classes de respostas afectivas que outras classes de caracteres naturais geram 12 © Joana Lúcio Paulo
  • 13. Características de um Agente Pedagógico •  Arquitectura 13 © Joana Lúcio Paulo
  • 14. Características de um Agente Pedagógico •  Arquitectura 14 © Joana Lúcio Paulo
  • 15. Características de um Agente Pedagógico •  Arquitectura 15 © Joana Lúcio Paulo
  • 16. Em que é que se baseiam os agentes pedagógicos? •  Affective Computing •  Artificial Intelligence •  Gesture and Narrative Language •  Intelligent Tutoring Systems •  Software Agents •  Synthetic Lifelike Characters 16 © Joana Lúcio Paulo
  • 17. Agentes Pedagógicos: Sumário •  O que são agentes Pedagógicos? •  Características de um Agente Pedagógico •  Exemplos de Agentes Pedagógicos •  Trabalho Futuro •  Considerações Finais •  Referências 17 © Joana Lúcio Paulo
  • 18. Cosmo the Pedagogical Agent of the Internet Advisor System North Carolina State University http://www.csc.ncsu.edu/eos/users/l/lester/www/imedia/IPA.html
  • 19. Cosmo, the Internet Advisor •  Cosmo inhabits the Internet Advisor, a learning environment for the domain of Internet packet routing. •  An impish, antenna-bearing creature who hovers about in the virtual world of routers and networks, he provides advice to students as they decide how to ship packets through the network to specified destinations. 19 © Joana Lúcio Paulo
  • 20. Behavior planning architecture 20 © Joana Lúcio Paulo
  • 21. Sample Cosmo posture 21 © Joana Lúcio Paulo
  • 22. Actions taken by Cosmo •  Congratulatory act •  Causal act •  Deleterious effect •  Background and assistance 22 © Joana Lúcio Paulo
  • 23. Herman, the bug the Pedagogical Agent of Design-A-Plant North Carolina State University http://www.csc.ncsu.edu/eos/users/l/lester/www/imedia/DAP.html
  • 24. Herman, the Bug is a knowledge-based learning environment project to investigate interactive problem-solving with animated pedagogical agents within the design-centered learning paradigm. 24 © Joana Lúcio Paulo
  • 25. With Design-A-Plant, students learn about botanical anatomy and physiology by graphically assembling customized plants that can thrive in specified environmental conditions. 25 © Joana Lúcio Paulo
  • 26. Two Kinds of Behavior: Advisory/ Explanatory Behaviors and Believability-Increasing Behaviors Advisory and explanatory behaviors are executed if: (1) The student requests advice (2) The student performs a problem-solving action (3) The student's problem-solving idle time exceeds anallotted interval Believability-enhancing behaviors are performed to satisfy the quot;situated livenessquot; criterion. 26 © Joana Lúcio Paulo
  • 27. Behavior Sequencing Engine Behavior Space Behavior Sequencing Engine Selection Assembly • Behavior History • Partial Solution • Current Problem • Problem History Problem Solving Context Problem Solving Environment User Action Global Behavior 27 © Joana Lúcio Paulo
  • 28. Sequencing of Advisory and Explanatory Behaviors Advisory and explanatory behaviors are sequenced by a coherence-based approach that entails: (1) Behavior Space Construction The behavior space contains animated segments and audio clips that are manually designed by a multidisciplinary team of graphic artists, animators, musicians and voice specialists. (2) Behavior Space Structuring The behavior space is structured using a tripartite behavior index of ontological, intentional, and rhetorical indices, prerequisite relationships, and continuity metric. (3) Dynamic Behavior Sequencing A runtime, a pedagogical sequencing engine selects and assembles behaviors by exploiting the coherence structure of the behavior space. 28 © Joana Lúcio Paulo
  • 29. Sequencing of Believability-Enhancing Behaviors •  The pedagogical sequencing engine is complemented by a believability-enhancing behavior sequencing engine. •  Believability-enhancing behaviors compete with each other for the right to be exhibited. •  At each moment, the strongest eligible behavior is heuristically selected as the winner and exhibited. 29 © Joana Lúcio Paulo
  • 30. Summary: Herman, the Bug •  Coherence-based approach to dynamically assemble advisory and explanatory behaviors •  Competition-based approach to select believability-enhancing behavior sequences •  Behavior sequences are designed by a multidisciplinary team of graphic artists, animators, musicians and voice specialists. On the one hand, the approach enable the production of high-quality presentations. On the other hand, enormous manual effort is required to design the behavior sequences for the behavior space. 30 © Joana Lúcio Paulo
  • 31. Adele the Pedagogical Agent for Medicine Students North Carolina State University http://www.isi.edu/isd/ADE/ade-body.html Apresentação cedida por Jeff Rickel (rickel@ISI.EDU)
  • 32. Adele •  Designed for use with Web-based courses •  Application: case-based health science •  Adele tutors students as they solve problems –  Monitors their actions –  Provides advice, rationales, hints, feedback –  Intervenes if the student makes serious mistakes –  Evaluates student performance –  Records student performance for later review 32 © Joana Lúcio Paulo
  • 33. Adele •  Designed for use with Web-based courses •  Application: case-based health science •  Adele tutors students as they solve problems –  Monitors their actions –  Provides advice, rationales, hints, feedback –  Intervenes if the student makes serious mistakes –  Evaluates student performance –  Records student performance for later review 33 © Joana Lúcio Paulo
  • 34. Example: Clinical Decision Making 34 © Joana Lúcio Paulo
  • 35. Example: Clinical Decision Making 35 © Joana Lúcio Paulo
  • 36. Example: Clinical Decision Making 36 © Joana Lúcio Paulo
  • 37. Example: Clinical Decision Making 37 © Joana Lúcio Paulo
  • 38. Example: Clinical Decision Making 38 © Joana Lúcio Paulo
  • 39. Example: Clinical Decision Making 39 © Joana Lúcio Paulo
  • 40. Example: Clinical Decision Making 40 © Joana Lúcio Paulo
  • 41. Example: Trauma Care 41 © Joana Lúcio Paulo
  • 42. A portion of a Bayes net for the Cough case 42 © Joana Lúcio Paulo
  • 43. Hint generation based on focus of attention 43 © Joana Lúcio Paulo
  • 44. Status •  Cases developed for 3 subjects: –  Clinical decision making in medicine –  Emergency trauma care –  Geriatric dentistry •  Classroom evaluations performed at USC School of Medicine, School of Dentistry •  School of Medicine plans to use Adele as part of massive curriculum reform effor 44 © Joana Lúcio Paulo
  • 45. Steve the Pedagogical Agent for Individual and Team Training North Carolina State University http://www.isi.edu/isd/VET/vet.html Apresentação cedida por Jeff Rickel (rickel@ISI.EDU)
  • 46. STEVE: A Virtual Human for Individual and Team Training Jeff Rickel in collaboration with W. Lewis Johnson, Marcus Thiebaux, Richard Angros, Ben Moore, Lockheed Martin, USC Behavioral Technology Laboratories Funded by the Office of Naval Research and the Army Research Office 46 © Joana Lúcio Paulo
  • 47. Training in Virtual Reality •  Distributed virtual environments offer low-cost, realistic training practically anywhere & anytime •  People are a key resource in such training –  instructors –  teammates –  adversaries •  People become a training bottleneck –  Not always available when needed •  Solution: Virtual Humans 47 © Joana Lúcio Paulo
  • 48. STEVE: A Virtual Human for Training •  Cohabits virtual world with students to serve as instructor or teammate •  Supports face-to-face interaction –  Navigational guidance –  Team collaboration –  Interactive demonstration and monitoring •  Behavior not scripted –  General capabilities for task-oriented collaboration (e.g., planning, dialogue) –  Domain-specific task knowledge represented as hierarchical plans 48 © Joana Lúcio Paulo
  • 49. Virtual Reality Architecture Human Interface Visual Interface Audio Effects Speech Recognition Speech Synthesis Message Dispatcher Steve Simulation Steve Agent Agent STEVE 49 © Joana Lúcio Paulo
  • 50. STEVE’s Architecture Cognition STEVE Domain knowledge General capabilities Motor commands Current state Translate into Filter, assemble Motor movements, speech into coherent view Perception Control Broadcast to Monitor events environment Commands to Event notifications environment Virtual Environment 50 © Joana Lúcio Paulo
  • 51. STEVE’s Cognitive Capabilities •  Planning, replanning, and plan execution •  Student monitoring •  Question answering •  Episodic memory •  Collaborative, mixed initiative dialogue •  Communication with teammates •  Learning by demonstration and experimentation •  Control of a graphical body 51 © Joana Lúcio Paulo
  • 52. What Steve perceives •  State of the world •  Actions taken by students or other agents •  Position of the student •  Student’s field of view 52 © Joana Lúcio Paulo
  • 53. Use of Head-Mounted Displays, 3D-Mouse and Dataglove 53 © Joana Lúcio Paulo
  • 54. Interaction with the Virtual World Steve controls the virtual world by sending commands to the simulation system VRIDES. Steve perceives the virtual world by receiving messages from VRIDES. 54 © Joana Lúcio Paulo
  • 55. Learning Environment with 2 Steve Agents Steve may appear in the virtual world as 3D-Character (Use of the Jack-Software) or as a hand. In the figure, Steve observes another agent at a routine task. 55 © Joana Lúcio Paulo
  • 56. Menu-Based Interface 56 © Joana Lúcio Paulo
  • 57. STEVE’s Nonverbal Capabilities •  Demonstrating actions •  Providing navigational guidance –  Collision-free path planning •  Guiding attention –  Gaze at objects –  Deictic gesture (pointing) –  Body orientation •  Giving feedback through head nods –  Unobtrusive tutorial feedback –  Acknowledge understanding of a teammate’s utterance •  Using gaze as a social signal –  Speaking to someone –  Listening to someone –  Waiting for someone 57 © Joana Lúcio Paulo
  • 58. STEVE’s Action Selection Criteria •  Dynamic task-oriented collaboration •  Physical context –  Locations of objects and STEVE –  Student’s field of view –  State of virtual world •  Task context –  Task model –  Current plan •  Collaboration context –  Current speaker –  Task initiative –  Status of current step –  Focus stack –  Previous actions and utterances 58 © Joana Lúcio Paulo
  • 59. Teaching STEVE (Richard Angros) •  Human teacher demonstrates task –  Perform and describe actions •  STEVE experiments in virtual environment –  Try variants of task –  Use machine learning to uncover dependencies among actions •  STEVE discusses task with human teacher –  Verify inductive hypotheses –  Discuss failures •  This approach combines several methods –  Programming by demonstration –  Machine learning –  Knowledge acquisition 59 © Joana Lúcio Paulo
  • 60. Status of STEVE •  Tested on a variety of shipboard team tasks –  Largest task involves 5 teammates handling a loss of fuel oil pressure –  The task involves a variety of subtasks involving individuals and sub-teams - about 3 dozen actions –  Tasks can involve any combination of people and agents •  Current project: Mission Rehearsal Exercise –  Funded by the Army Research Office through the USC Institute for Creative Technologies –  Research focus: Extend Steve to include emotions, more sophisticated natural language understanding, and more realistic body •  Contact: Dr. Jeff Rickel, USC Information Sciences Institute, rickel@isi.edu; http://www.isi.edu/isd/carte 60 © Joana Lúcio Paulo
  • 61. Agentes Pedagógicos: Sumário •  O que são agentes Pedagógicos? •  Características de um Agente Pedagógico •  Exemplos de Agentes Pedagógicos •  Trabalho Futuro •  Considerações Finais •  Referências 61 © Joana Lúcio Paulo
  • 62. Quem está a trabalhar nisto? •  Centros de Investigação – Center for Advanced Research in Technology for Education (CARTE) – Intellimedia •  Companhias privadas – Artificial Life – Extempo Systems – Microsoft Agent Group 62 © Joana Lúcio Paulo
  • 63. Mission Rehearsal Exercise Project •  Funded by Army Research Office •  Integrates high-fidelity graphics, audio, and virtual humans for training scenarios •  STEVE agents interact with human students as coach, teammates, and extras 63 © Joana Lúcio Paulo
  • 64. Virtual Labs for Science/Engineering •  Automated Lab Instructor (ALI) –  Collaboration with USC Chemistry Department –  Students run simulated science experiments –  ALI recognizes learning opportunities, quizzes students, provides explanations •  Virtual Factory Teaching System –  Funded by NSF –  Collaboration with USC Computer Science, USC Industrial and Systems Engineering, and outside universities –  Students make decisions to run virtual factory –  Intelligent agent will recognize learning opportunities, quiz students, and provide explanations 64 © Joana Lúcio Paulo
  • 65. Agentes Pedagógicos na Web 65 © Joana Lúcio Paulo
  • 66. Agentes Pedagógicos: Sumário •  O que são agentes Pedagógicos? •  Características de um Agente Pedagógico •  Exemplos de Agentes Pedagógicos •  Trabalho Futuro •  Considerações Finais •  Referências 66 © Joana Lúcio Paulo
  • 67. Considerações Finais •  Agentes Pedagógicos são agentes autónomos que apoiam a aprendizagem humana, interactuando com os alunos em ambientes de aprendizagem interactivos. •  Exemplos: –  Cosmo –  Herman, the bug –  Adele –  Steve •  Principais características de um agente pedagógico 67 © Joana Lúcio Paulo
  • 68. Agentes Pedagógicos: Sumário •  O que são agentes Pedagógicos? •  Características de um Agente Pedagógico •  Exemplos de Agentes Pedagógicos •  Trabalho Futuro •  Considerações Finais •  Referências 68 © Joana Lúcio Paulo
  • 69. Referências e endereços electrónicos • Além dos que foram sendo referidos: – http://www.isi.edu/isd/VET/steve-demo.html – http://www.isi.edu/isd/carte/carte-demos.htm – http://san.stanford.edu/~g345/iapa/main.htm – http://www.mcc.com/projects/c3/presentations/johnson – http://www.csc.ncsu.edu/eos/users/l/lester/www/imedia 69 © Joana Lúcio Paulo
  • 70. Perguntas? Objecções? Clarificações? Interrogações? Interpolações? Contrapropostas? Observações? Têm algo a dizer??? 70 © Joana Lúcio Paulo
  • 71. Ou já estão todos a dormir??? 71 © Joana Lúcio Paulo