Embed Size (px)
DESCRIPTION
In this paper, we propose an analytical model for computing the utility of ESP games, i.e., the throughput rate of appropriate labels for given images. The model targets generalized games, where the number of players, the consensus threshold, and the stopping condition are variable. Via extensive simulations, we show that our model can accurately predict the stopping condition that will yield the optimal utility of an ESP game under a specific game setting. A service provider can therefore utilize the model to ensure that the hosted ESP games produce high-quality labels efficiently, given that the number of players willing to invest time and effort in the game is limited.
Citation preview
Chien-Wei Lin, Kuan-Ta Chen, Ling-Jyh ChenAcademia Sinica
Irwin King, and Jimmy LeeThe Chinese University of Hong Kong
MotivationMotivationESP games annotate images on the web.
Can we optimize the goodput of the game?
Service provider can utilize our model to improve their system.
IdeaIdeaModel the performance of the game and optimize it.
A more generalized ESP game.The number of players can be more than 2.
The consensus threshold can be any positive integer, but not larger than the number of players.
The stopping condition can be more than 1.
3 Collaborative Quantity3 Collaborative QuantityEfficiency
the rate that labels are matched.
Quality the proportion of good labels among all matched labels.
Utility the throughput rate of good labels.Utility = Efficiency × Quality
Assumption of ModelAssumption of ModelRound-based play
Make only one guess in each round.
Independent guessCurrent guess is not affected by previous guesses.
Good and bad wordsThe sizes of good and bad words are both limited.Players will do their best to guess good words.
Uniform guessThe guess is made uniformly.
Parameters in the ModelParameters in the ModelNumber of players
denoted as n.
Consensus thresholddenoted as m.
Size of good vocabularydenoted as .
Probability of guessing good wordsdenoted as .
Stopping condition Stopping condition is our main variable.
goodv
goodprob
Model ValidationModel ValidationTrade-off between efficiency & quality.
Validate the model by simulations.
Optimal Stopping ConditionsOptimal Stopping ConditionsOptimal stopping condition changes under different parameter settings.
Benefit of OptimizationBenefit of OptimizationWe provide twice as much utility as a non-optimized game.
ContributionContributionModel for generalized games.
Propose an optimal termination condition to optimize the system.
Game providers can utilize our model to maximize the outcome of games.
Thank You!Thank You!
