Agentes Pedagogicos

Joana Paulo, nº 44048
Joana.Paulo@RNL.IST.UTL.Pt

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© Joana Lúcio Paulo

Agentes Pedagógicos: Sumário
•  O que são agentes Pedagógicos?
•  Características de um Agente
Pedagógico
•  Exemplos de Agentes Pedagógicos
•  Trabalho Futuro
•  Considerações Finais
•  Referências

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Pedagógico
•  Referências

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O que são Agentes Pedagógicos?

Agente?

Pedagógico?

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•  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.

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Os Agentes Pedagógicos

AGENTES

Biológicos Robóticos Computacionais

de Software Vida Artificial

Tarefas Específicas de Entretenimento Pedagógicos
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•  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 

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A Pedagogia implica
•  Alguém que
saiba ensinar
e/ou
acompanhar

•  Alguém que
queira
aprender
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Agentes Pedagógicos são
agentes autónomos que
apoiam a aprendizagem humana,
interactuando com os alunos
em ambientes de aprendizagem
interactivos.

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Pedagógico
•  Referências

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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
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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
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•  Arquitectura

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•  Arquitectura

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•  Arquitectura

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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
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Pedagógico
•  Referências

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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

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.
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Behavior planning architecture

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Sample Cosmo posture

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Actions taken by Cosmo

•  Congratulatory act
•  Causal act
•  Deleterious effect
•  Background and
assistance

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Herman, the bug

of
Design-A-Plant

http://www.csc.ncsu.edu/eos/users/l/lester/www/imedia/DAP.html

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.

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With Design-A-Plant,
students learn about
botanical anatomy
and physiology by
graphically

assembling
customized plants
that can thrive in
specified
environmental
conditions. 25

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.

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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

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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

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.
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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.
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Adele

for
Medicine Students

http://www.isi.edu/isd/ADE/ade-body.html

Apresentação cedida por
Jeff Rickel (rickel@ISI.EDU)

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
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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

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Example: Clinical Decision Making

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Example: Trauma Care

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A portion of a Bayes net
for the Cough case

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Hint generation based
on focus of attention

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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
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Steve
the Pedagogical
Agent for Individual
and Team Training


http://www.isi.edu/isd/VET/vet.html

Apresentação cedida por
Jeff Rickel (rickel@ISI.EDU)

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

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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
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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
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Virtual Reality Architecture
Human Interface

Visual Interface Audio Effects Speech Recognition Speech Synthesis

Message Dispatcher

Steve
Simulation Steve
Agent
Agent
STEVE

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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
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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

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What Steve perceives
•  State of the world
•  Actions taken by students or other agents
•  Position of the student
•  Student’s field of view

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Use of Head-Mounted Displays,
3D-Mouse and Dataglove

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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.

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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.
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Menu-Based Interface

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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
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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
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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

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

Pedagógico
•  Referências

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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

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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

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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
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Agentes Pedagógicos na Web

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Pedagógico
•  Referências

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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

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Pedagógico
•  Referências

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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

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Perguntas?
Objecções?
Clarificações?
Interrogações?
Interpolações?
Contrapropostas?
Observações?

Têm algo a dizer???
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Ou já estão todos a dormir???

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