On the Implications of Lookahead Search in Game Playing
Vahab Mirrokni, Nithum Thain, Adrian Vetta

TL;DR
This paper analyzes the theoretical performance of k-lookahead search in game playing, examining its impact on outcomes and behaviors across various strategic settings.
Contribution
It provides a formal analysis of lookahead search's effects on game outcomes and agent behaviors in diverse applications.
Findings
Lookahead search influences social welfare outcomes.
Agents exhibit strategic behaviors based on search depth.
Performance varies with game type and search parameters.
Abstract
Lookahead search is perhaps the most natural and widely used game playing strategy. Given the practical importance of the method, the aim of this paper is to provide a theoretical performance examination of lookahead search in a wide variety of applications. To determine a strategy play using lookahead search}, each agent predicts multiple levels of possible re-actions to her move (via the use of a search tree), and then chooses the play that optimizes her future payoff accounting for these re-actions. There are several choices of optimization function the agents can choose, where the most appropriate choice of function will depend on the specifics of the actual game - we illustrate this in our examples. Furthermore, the type of search tree chosen by computationally-constrained agent can vary. We focus on the case where agents can evaluate only a bounded number, , of moves into the…
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Taxonomy
TopicsGame Theory and Applications · Auction Theory and Applications · Artificial Intelligence in Games
