249,047 research outputs found
Learning to Play Games in Extensive Form by Valuation
A valuation for a player in a game in extensive form is an assignment of
numeric values to the players moves. The valuation reflects the desirability
moves. We assume a myopic player, who chooses a move with the highest
valuation. Valuations can also be revised, and hopefully improved, after each
play of the game. Here, a very simple valuation revision is considered, in
which the moves made in a play are assigned the payoff obtained in the play. We
show that by adopting such a learning process a player who has a winning
strategy in a win-lose game can almost surely guarantee a win in a repeated
game. When a player has more than two payoffs, a more elaborate learning
procedure is required. We consider one that associates with each move the
average payoff in the rounds in which this move was made. When all players
adopt this learning procedure, with some perturbations, then, with probability
1, strategies that are close to subgame perfect equilibrium are played after
some time. A single player who adopts this procedure can guarantee only her
individually rational payoff
Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games
With the recent advances in solving large, zero-sum extensive form games,
there is a growing interest in the inverse problem of inferring underlying game
parameters given only access to agent actions. Although a recent work provides
a powerful differentiable end-to-end learning frameworks which embed a game
solver within a deep-learning framework, allowing unknown game parameters to be
learned via backpropagation, this framework faces significant limitations when
applied to boundedly rational human agents and large scale problems, leading to
poor practicality. In this paper, we address these limitations and propose a
framework that is applicable for more practical settings. First, seeking to
learn the rationality of human agents in complex two-player zero-sum games, we
draw upon well-known ideas in decision theory to obtain a concise and
interpretable agent behavior model, and derive solvers and gradients for
end-to-end learning. Second, to scale up to large, real-world scenarios, we
propose an efficient first-order primal-dual method which exploits the
structure of extensive-form games, yielding significantly faster computation
for both game solving and gradient computation. When tested on randomly
generated games, we report speedups of orders of magnitude over previous
approaches. We also demonstrate the effectiveness of our model on both
real-world one-player settings and synthetic data
A New Game Equivalence and its Modal Logic
We revisit the crucial issue of natural game equivalences, and semantics of
game logics based on these. We present reasons for investigating finer concepts
of game equivalence than equality of standard powers, though staying short of
modal bisimulation. Concretely, we propose a more finegrained notion of
equality of "basic powers" which record what players can force plus what they
leave to others to do, a crucial feature of interaction. This notion is closer
to game-theoretic strategic form, as we explain in detail, while remaining
amenable to logical analysis. We determine the properties of basic powers via a
new representation theorem, find a matching "instantial neighborhood game
logic", and show how our analysis can be extended to a new game algebra and
dynamic game logic.Comment: In Proceedings TARK 2017, arXiv:1707.0825
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