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Curious Places

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A Curious, Virtual, Sentient Room. Limitations and Future Work. The computer for the 21st century ... A Curious, Virtual, Sentient Room. Floor Sensors ... – PowerPoint PPT presentation

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Title: Curious Places


1
Curious Places
  • A Room that Adapts using Curiosity and Supervised
    Learning

Kathryn Merrick, Mary Lou Maher Rob Saunders
October, 2007 Key Centre of Design Computing and
Cognition, University of Sydney
2
Overview
  • Adaptable, Intelligent Environments
  • Curious Supervised Learning
  • A Curious, Virtual, Sentient Room
  • Limitations and Future Work

3
Adaptable, Intelligent Environments
  • The computer for the 21st century
  • Hundreds of computers per room
  • Computers come and go
  • (Weiser, 1991)
  • Adaptability is important at two levels
  • The middleware level
  • The behaviour level

4
Adaptable Middleware
  • Resource management and communication
  • Adaptability has been widely considered at this
    level
  • Real time interaction
  • Presence services
  • Ad hoc networking

Intelligent Room Project
Gaia
BlipSystems
5
Adaptable Behaviour
  • Adapting behaviour to human activities
  • Supervised Learning
  • The Neural Network House
  • Data mining
  • Considered in fixed domains
  • How can we achieve adaptive behaviour in response
    to changing hardware or software?

6
Adaptability by Curiosity and Learning
  • Curiosity adapts focus of attention to relevant
    learning goals
  • Learning adapts behaviour to fulfil goals
  • Curious reinforcement learning
  • Curious supervised learning

MySQL Database
Agent
Projector
Rear projection screen
Curious Information Display
Curious Research Space
PC
Agent
Bluetooth blip nodes
7
Supervised Learning
  • Learning from examples
  • A supervised learning problem P can be
    represented formally by
  • A set S of sensed states
  • A set A of actions
  • A set X of examples Xi (Si, Ai)
  • A policy p S ? A

8
Complex, Dynamic Environments
  • Contain multiple learning problems
  • P P1, P2, P3
  • Learning problems in P may change over time
  • Addition of new problems
  • Removal of obsolete problems

9
Modelling Curiosity for Supervised Learning
  • Aim to focus attention on states, actions and
    examples from a subset of problems
  • Works by filtering
  • Identify potential tasks to
  • learn or act upon
  • Compute curiosity values
  • Arbitrate on what to filter
  • High curiosity may trigger
  • learning or action
  • Low curiosity does not

S(t), X(t)
Curiosity
Observations and events
Task Selection
Curiosity Value
Arbitration
S(t)
X(t)
Learning
Action
10
The Curious Supervised Learning Agent
  • Past states, examples and actions are stored in
    an experience trajectory Y
  • Experiences may influence curiosity

11
A Curious, Virtual, Sentient Room
  • A university meeting room in Second Life
  • Seminars and Meetings
  • Tutorials
  • Skype-conferencing

12
Virtual Sensors and Effectors
  • Floor Sensors
  • SmartBoard and Chairs
  • Blip System
  • Lights

13
Meta-Sensors and Meta-Effectors
  • Agent does not communicate directly with sensors
    and effectors
  • Agent has a sensor of sensors and an effector
    of effectors
  • BlipSystem provides an up-to-date list of current
    sensors and effectors and acts as an intermediary
    for communication

14
The Curious Room Agent
  • Computational model of novelty used for curiosity
  • Table-based supervised learning using
    associations
  • Learns accurately but
  • Unable to generalise

15
Behaviour of the Curious Place
  • Avatar enters ? Lights go on
  • Avatar sits ? SmartBoard on ? Lights off

16
Preliminary Evaluation
  • 6 repetitions by human controlled avatars
    required for learning
  • Can adapt to new devices
  • Can adapt simple behaviours to form more complex
    sequences

17
Limitations
  • Current prototype is proof-of-concept only, no
    significant empirical results yet
  • Issue of if/when/how to forget behaviours
  • Is an interface required for manual editing or
    override of learned behaviours?

18
Future Work
  • Further work on curiosity models
  • Design a suite of experiments to test attention
    focus in
  • Environments of increasing complexity
  • Dynamic environments
  • More complex tasks
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