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Urban Encounters: The game of real life
Eamonn O’NeillUniversity of Bath
Department of Computer Science
Vassilis KostakosUniversity of Madeira / Carnegie
Mellon University
CHI 2008 Proceedings Works In Progress
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Outline
• Introduction• Modelling human encounter in urban space• The game of real life• Describing the cell dynamics• Modelling life, death and survival• Discussion• Conclusion and ongoing work
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Introduction(1/2)
• A game that helps us model urban encounter• Developing models of human movement and
patterns of encounter in cities• Aim:– a systemic understanding of cities and urban life– use this understanding to aid in the development
of urban pervasive applications
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Introduction(2/2)
• “The Game of Real Life” is an extension of Conway’s Game of Life
• User plays game on the mobile device and the game gathers empirical data by Bluetooth
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Modelling human encounter in urban space
• Models can be broadly categorised into three levels:– Macro– Meso– Micro
• Applications of encounter– Delay Tolerant Networks– a mobile application
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The game of real life(1/4)
• Understanding the patterns of urban encounters
• The Game of Life as a basis for simulating how people encounter and interact with each other
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The game of real life(2/4)
• The game of life:– Survival– Death– Life
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The game of real life(3/4)
• The game of real life:– The playing board becomes an undirected graph– Cells are not limited to two states
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The game of real life(4/4)
• Address two issues:– define the set of neighbours that a cell can have at
iteration n of the game.– define the rules that drive life, death and survival.
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Describing the cell dynamics
• Data-gathering mobile application that utilises Bluetooth
• Implements a version of the Game of Real Life in order to motivate users to keep it running on their mobile devices.
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Modelling life, death and survival(1/2)
• A cell represents a specific mobile Bluetooth device
• The number of neighbouring devices is given by the results of a Bluetooth discovery scan
• The cell’s state may decrease or increase• Analysed a week’s data from three participants• From this data we derived a set of rules for
changing the cells’ state
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Modelling life, death and survival(2/2)
• Identified as optimum the following rules:– Under-crowding is 2 or fewer neighbouring
Bluetooth devices– Desirable number of neighbouring devices is 3-5– Over-crowding is 6 or more neighbouring devices
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Modelling life, death and survival(1/2)
• Adding memory to the cell
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Modelling life, death and survival(2/2)
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Discussion(1/2)
• Inevitably most of the time was spent on death (states 0 and 1) and state 6, which was the maximum possible.
• The states between the two extremes typically act as buffers and are occupied only temporarily
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Discussion(2/2)
• Identified that adding memory to the cells produces graphs with smaller and fewer fluctuations.
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Conclusion and ongoing work(1/2)
• Describe extensions to the game that make it more closely resemble human encounters
• Present a mobile application that acts both as a game for users and a data collection tool.
• Cell spend most time on the extreme states• Equipping each cell with memory enables it to
predict its state by utilising its own memory
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Conclusion and ongoing work(2/2)
• This paper provides the groundwork for developing such an account.
• Attempting to determine the asymptotic behaviour of our model as influenced by different set of values for life and death
• Ultimately develop a mathematical account of cell dynamics that closely matches the encounters recorded by our Bluetooth scanners.