“Human Computation” represents a new paradigm of applications that take advantage of people’s desire to be entertained and produce useful metadata as a by-product. By creating games with a purpose, human computation has shown promise in solving a variety of problems that computer computation cannot currently resolve completely. Using the ESP game as an example, we propose a metric, called system gain, for evaluating the performance of human computation systems, and also use analysis to study the properties of the ESP game. We argue that human computation systems should be played with a strategy. To this end, we implement an Optimal Puzzle Selection Strategy (OPSA) based on our analysis to improve human computation. Using a comprehensive set of simulations, we demonstrate that the proposed OPSA approach can effectively improve the system gain of the ESP game, as long as the number of puzzles in the system is sufficiently large.

1. An Analytical Study of Puzzle
Selection Strategies for the ESP Game
Ling-Jyh Chen, Bo-Chun Wang, Kuan-Ta Chen
Academia Sinica
Irwin King, and Jimmy Lee
The Chinese University of Hong Kong

2. How the ESP Game Works?
PLAYER 1 PLAYER 2
GUESSING: CAR GUESSING: BOY
GUESSING: HAT GUESSING: CAR
GUESSING: KID
SUCCESS! SUCCESS!
YOU AGREE ON CAR YOU AGREE ON CAR

3. Why is it Important?
• Some statistics (July 2008)
– 200,000+ players have contributed 50+ million
labels.
– Each player plays for a total of 91 minutes.
– The throughput is about 233
labels/player/hour (i.e., one label every 15
seconds)

4. The Ideas behind the ESP Game
• “Human Computation” represents a new
paradigm of applications.
– solve some problems which are difficult to be solved
by computers
• “Games With A Purpose” (GWAP) – by Dr.
Luis von Ahn (CMU)
– take advantage of people's desire to be entertained
– produce useful metadata as a by-product

5. Our Contributions
• We propose an evaluation metric for human computation
systems.
• We study the inner properties of the ESP game using analysis.
• We propose an “Optimal Puzzle Selection Algorithm” (OPSA).
• We present a comprehensive set of simulation results.
• Our model is easy and applicable to other ESP-like systems.

6. The Main Ideas
• There are two goals :
– the system prefers to maximize the number of
puzzles which have been played
– the system prefers to take as many labels as
possible for each puzzle
• Unfortunately, there is a trade-off between
the two goals.

7. The Proposed Metric
System Gain ( G )
= ln ( N ) × ln ( S N )
the number of the puzzles thatsum ofbeen played labels
the score average scores per puzzle
the have all produced
T T S
r= ⇒N= = E[S] × r
N
N r r : each puzzle should be played
T : total played rounds how many times in average
r : each puzzle should be played E[S] : the expected score value of
how many times in average each label

8. The Proposed Metric
System Gain ( G )
= ln ( N ) × ln ( S N )
= l n (T r ) × l n ( E [ S ] × r )
l n ( T ) − l n ( E [ S ]) ⎞
2
⎛
= − ⎜ ln ( r ) − ⎟ +C
⎝ 2 ⎠
ln (T ) − ln ( E [ S ])
Maximum G = C , when r = e 2

9. The Proposed Metric
Maximum G
ln (T ) − ln ( E [ S ])
r=e 2

10. Puzzle Selection Algorithms
• Optimal Puzzle Selection Algorithm (OPSA)
– select a puzzle based on our analysis
• Random Puzzle Selection Algorithm (RPSA)
– select a puzzle by random
• Fresh-first Puzzle Selection Algorithm (FPSA)
– select a puzzle that has been played least
frequently

11. Evaluation
• Using Monte Carlo Simulations
• # of rounds: T = 10,000
• # of puzzles: 100 <= M <= 5,000

12. Conclusion
• We have proposed a metric to evaluate
the performance of GWAP.
• We argue that GWAP needs to be “played
with a strategy”.
• We propose the Optimal Puzzle Selection
Algorithm (OPSA) for ESP-like games.

13. Thank You!
http://nrl.iis.sinica.edu.tw