Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Game Design for Eliciting Distinguishable Behavior   The ability to inferring latent psychological traits from human behavior is
key to developing personalized human-interacting machine learning systems.
Approaches to infer such traits range from surveys to manually-constructed
experiments and games. However, these traditional games are limited because
they are typically designed based on heuristics. In this paper, we formulate
the task of designing \emph{behavior diagnostic games} that elicit
distinguishable behavior as a mutual information maximization problem, which
can be solved by optimizing a variational lower bound. Our framework is
instantiated by using prospect theory to model varying player traits, and
Markov Decision Processes to parameterize the games. We validate our approach
empirically, showing that our designed games can successfully distinguish among
players with different traits, outperforming manually-designed ones by a large
margin.

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The categories of this paper are: q-alg
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc