110,809 research outputs found
BridgeHand2Vec Bridge Hand Representation
Contract bridge is a game characterized by incomplete information, posing an
exciting challenge for artificial intelligence methods. This paper proposes the
BridgeHand2Vec approach, which leverages a neural network to embed a bridge
player's hand (consisting of 13 cards) into a vector space. The resulting
representation reflects the strength of the hand in the game and enables
interpretable distances to be determined between different hands. This
representation is derived by training a neural network to estimate the number
of tricks that a pair of players can take. In the remainder of this paper, we
analyze the properties of the resulting vector space and provide examples of
its application in reinforcement learning, and opening bid classification.
Although this was not our main goal, the neural network used for the
vectorization achieves SOTA results on the DDBP2 problem (estimating the number
of tricks for two given hands)
Improving Search with Supervised Learning in Trick-Based Card Games
In trick-taking card games, a two-step process of state sampling and
evaluation is widely used to approximate move values. While the evaluation
component is vital, the accuracy of move value estimates is also fundamentally
linked to how well the sampling distribution corresponds the true distribution.
Despite this, recent work in trick-taking card game AI has mainly focused on
improving evaluation algorithms with limited work on improving sampling. In
this paper, we focus on the effect of sampling on the strength of a player and
propose a novel method of sampling more realistic states given move history. In
particular, we use predictions about locations of individual cards made by a
deep neural network --- trained on data from human gameplay - in order to
sample likely worlds for evaluation. This technique, used in conjunction with
Perfect Information Monte Carlo (PIMC) search, provides a substantial increase
in cardplay strength in the popular trick-taking card game of Skat.Comment: Accepted for publication at AAAI-1
Code Creation in Endogenous Merger Experiments
We study the conflict that can occur in a merger due to firms’ use of specialized
language, or “code,” and whether participants accurately forecast this difficulty. After
creating a shared code to describe different pictures accurately, subjects bid for extra
payments to join a merged group. The two lowest bidders are placed in the merged
group. Values inferred from two different bidding procedures indicate fairly accurate
general appraisals of the cost of the merger, but the values of those subjects who
bid the least, and choose to join the merged group, are too optimistic, reflecting an
“organizational winner’s curse.
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