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Grim-trigger strategies are a fundamental mechanism for sustaining equilibria in iterated games: the players cooperate along an agreed path, and as soon as one player deviates, the others form a coalition to play him down to his minmax level. A precondition to triggering such a strategy is that the identity of the deviating player becomes common knowledge among the other players. This can be difficult or impossible to attain in games where the information structure allows only imperfect monitoring of the played actions or of the global state. We study the problem of synthesising finite-state strategies for detecting the deviator from an agreed strategy profile in games played on finite graphs with different information structures. We show that the problem is undecidable in the general case where the global state cannot be monitored. On the other hand, we prove that under perfect monitoring of the global state and imperfect monitoring of actions, the problem becomes decidable, and we present an effective synthesis procedure that covers infinitely repeated games with private monitoring.
We study a stochastic game framework with dynamic set of players, for modeling and analyzing their computational investment strategies in distributed computing. Players obtain a certain reward for solving the problem or for providing their computatio
In this paper, we examine the Nash equilibrium convergence properties of no-regret learning in general N-player games. For concreteness, we focus on the archetypal follow the regularized leader (FTRL) family of algorithms, and we consider the full sp
Search has played a fundamental role in computer game research since the very beginning. And while online search has been commonly used in perfect information games such as Chess and Go, online search methods for imperfect information games have only
We study auctions for carbon licenses, a policy tool used to control the social cost of pollution. Each identical license grants the right to produce a unit of pollution. Each buyer (i.e., firm that pollutes during the manufacturing process) enjoys a
Counterfactual Regret Minimization (CFR) is an efficient no-regret learning algorithm for decision problems modeled as extensive games. CFRs regret bounds depend on the requirement of perfect recall: players always remember information that was revea