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Designing Games with a Purpose

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DESIGNING GAMES WITH A PURPOSE By Luis von Ahn and Laura Dabbish – PowerPoint PPT presentation

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Title: Designing Games with a Purpose


1
Designing Games with a Purpose
  • By Luis von Ahn and Laura Dabbish

2
Introducing games with a purpose
  • Many tasks are trivial for humans, but very
    challenging for computer programs
  • People spend a lot of time playing games
  • Idea
  • Computation Game Play
  • People playing GWAPs perform basic tasks that
    cannot be automated. While being entertained,
    people produce useful data as a side effect.

3
Related work
  • Recognized utility of human cycles and
    motivational power of gamelike interfaces
  • Open source software development
  • Wikipedia
  • Open Mind Initiative
  • Interactive machine learning
  • Incorporating game-like interfaces

4
Wanna Play???
5
THE QUESTION IS
  • How to design these games such that
  • People enjoy playing them!
  • They produce high quality outputs!

6
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9
Basic structure achieves several goals
  • Encourage players to produce correct outputs
  • Partially verify the output correctness
  • Providing an enjoyable social experience

10
Make GWAPS more entertaining How?
  • Introduce challenge
  • Introduce competition
  • Introduce variation
  • Introduce communication

11
Ensure output accuracy How?
  • Random matching
  • Player testing
  • Repetition
  • Taboo outputs

12
Other design issues
  • Pre-recorded Games
  • More than two players

13
How to judge GWAP success?
  • Expected Contribution
  • Throughput ? Average Lifetime Play

14
Conclusion and future work
  • First general method for integrating computation
    and game play!
  • (Everyone could/should contribute to AI
    progress!)
  • Other GWAP game types?
  • How do problems fit into GWAP templates?
  • How to motivate not only accuracy but creativity
    and diversity?
  • What kinds of problems fall outside of GWAP
    approach?

15
Questions? Comments?
  • What do you think of this approach in general?
    Which problems are suitable for this approach?
  • What do you love about these games? What are the
    inefficiencies in these games?
  • How do we make these games more enjoyable and
    more efficient in producing correct results?

16
A GAME-THEORETIC ANALYSIS OF THE ESP GAME
  • By Shaili Jain and David Parkes

17
Two Different Payoff Models
  • Match-early preferences
  • Want to complete as many rounds as possible
  • Reflect current scoring function in ESP game
  • Low effort is a Bayes-NE
  • Rarest-words-first preferences
  • Want to match on infrequent words
  • How can we accomplish this?

18
?
How can we assign scores to outcomes to promote
desired behaviours?
19
The Model
  • Universe of words
  • Words relevant to an image
  • The game designer is trying to learn this
  • Dictionary size
  • Sets of words for a player to sample from
  • Word frequency
  • Probability of word being chosen if many people
    were asked to state a word relating to this image
  • Order words according to decreasing frequency
  • Effort level
  • Frequent words correspond to low effort

20
The Model continued
  • Two stages of the game
  • 1st stage choose an effort level
  • 2nd stage choose a permutation on sampled
    dictionary
  • Only consider the strategies involving playing
    all words in the dictionary
  • Only consider consistent strategies
  • Specify a total ordering on elements and applying
    that ordering to the realized dictionary
  • Complete strategy effort level word ordering

21
More Definitions
  • A match first match
  • Probability of a match in a particular location
  • Outcome word location
  • Valuation function a total ordering on outcomes
  • Utility

22
Match-Early Preferences
  • Lemma 1 Playing ? is not an ex-post NE.
  • Proof
  • Player 2, D2 w2, w3 s2 play w2, then w3
  • Player 1, D1 w1, w2 s1 play w1, then w2
  • But, player 1 is better off playing w2
    first!

23
Match-Early Preferences
  • Definition 6 stochastic dominance for 2nd stage
    strategy
  • (Lemma 2, 3) Stochastic dominance is sufficient
    and necessary for utility maximization.
  • (Lemma 5, 6) Playing ? is a strict best response
    to an opponent who plays ?
  • Theorem 1 (?, ?) is a strict Bayesian-Nash
    equilibrium of the 2nd stage of the ESP game for
    match-early preferences.

24
Match-Early Preferences
  • Definition 6 stochastic dominance for 2nd stage
    strategy
  • Fix opponents strategy s2, stochastic dominance
  • Strategy s stochastically domiantes s ?
  • P(s, 1) P(s, k) gt P(s, 1) P(s, k),
    for all 1 lt k lt d

25
Match-Early Preferences
  • (Lemma 2, 3) Stochastic dominance is sufficient
    and necessary for utility maximization.
  • Proof by induction
  • Inductive step uses inductive hypothesis and
    stochastic dominance to establish result

26
MATCH-EARLY PREFERENCES
  • Key result (Lemma 4) Given effort level e,
  • D x, , D x, , f(x) lt f(x)
  • D and D only differ by the element x and x
  • P(sampling D) gt P(sampling D) for effort level e

27
Match-Early Preferences
  • (Lemma 5, 6) Playing ? is a strict best response
    to an opponent who plays ?
  • Proof by induction
  • Base case (Lemma 5) the probability of a first
    match in location 1 is strictly maximized when
    player 1 plays her most frequent word first.
  • Inductive step (Lemma 6) Suppose player 2 plays
    ?. Given that player 1 played her k highest
    frequency words first, the probability of a first
    match in locations 1 to k is strictly maximized
    when player 1 players her (k1)st highest
    frequency word next.

28
Match-Early Preferences
  • Proof for Lemma 5 and 6 (Idea use Lemma 4)
  • Want Pr(sampling D in A) gt Pr(sampling D in B)
  • f(wi) gt f(wi1)
  • A (wi highest word) C (no wi1) and D (has
    wi1)
  • B (wi1 highest word)
  • 1-to-1 mapping between C and B
  • P(sampling D in C) gt P(sampling D in B)

29
Match-Early Preferences
  • (Lemma 5, 6) Playing ? is a strict best response
    to an opponent who plays ?
  • Theorem 1 (?, ?) is a strict Bayesian-Nash
    equilibrium of the 2nd stage of the ESP game for
    match-early preferences.

30
MATCH-EARLY PREFERENCES CONTD
  • Definition 7 stochastic dominance for complete
    strategy
  • (Lemma 7, 8) Stochastic dominance is sufficient
    and necessary for utility maximization
  • (Lemma 12) Playing L stochastically dominates
    playing M.
  • Theorem 2 ((L, ?), (L, ?)) is a strict
    Bayesian-Nash equilibrium for the complete game.

31
MATCH-EARLY PREFERENCES CONTD
  • (Lemma 12) Playing L stochastically dominates
    playing M
  • Randomized mapping from DM to DL
  • D in DM is transformed by Take low words in DM,
    continue sampling from DL until we get enough
    words

32
MATCH-EARLY PREFERENCES CONTD
  • (Lemma 12) Playing L stochastically dominates
    playing M
  • Lemma 10 Each dictionary in DM is mapped to a
    dictionary in DL which is at least as likely to
    match against the opponents dictionary
  • Lemma 11 The probability of sampling D from DL
    is the same as the probability of getting D by
    sampling D from DM and then transform D into D
    under the randomized mapping.

33
Match-Early Preferences
  • Theorem 2 ((L, ?), (L, ?)) is a strict
    Bayesian-Nash equilibrium for the complete game.

34
RARE-WORDS-FIRST PREFERENCES
  • Definition 8 Rare-words first preferences
  • (Lemma 13, 14) Stochastic dominance is still
    sufficient and necessary for utility maximization
  • (Lemma 15) Suppose player 2 is playing ?. For
    any dictionary, no consistent strategy of player
    1 stochastically dominates all other consistent
    strategies.
  • (Lemma 16) Suppose player 2 is playing ?. For
    any dictionary, no consistent strategy of player
    1 stochastically dominates all other consistent
    strategies.

35
Rare-Words-First Preferences
  • Idea for proving Lemma 15 (and 16)
  • U w1, w2, w3, w4 d 2
  • D1 w1, w2 s1 w1, w2 s2 w2, w1
  • x Pr(D2 w2, w3 or D2 w2, w4)
  • y Pr(D2 w1, w2)
  • z Pr(D2 w1, w3 or D2 w1, w4)
  • s1 (0, x, yz, 0) s1 (x, y, 0, z)
  • Neither s1 nor s1 stochastically dominates the
    other

36
Future Work
  • Sufficient and necessary conditions for playing ?
    with high effort being a Bayesian-Nash
    equilibrium?
  • Incentive structure for high effort? - To extend
    the labels for an image
  • Other types of scoring functions?
  • Rules of Taboo words?
  • Consider entire sequence of words suggested
    rather than only focusing on the matched word?

37
Questions? Comments?
  • What do you think of the model? Does everything
    in the model make sense? Can you suggest
    improvements to the model?
  • What incentive structure could possibly lead to
    high effort? Would the use of Taboo words be
    useful for this purpose?
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