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Q-learning in single-agent environments is known to converge in the limit given sufficient exploration. The same algorithm has been applied, with some success, in multi-agent environments, where traditional analysis techniques break down. Using established dynamical systems methods, we derive and study an idealization of Q-learning in 2-player 2-action(More)
The field of multiagent decision making is extending its tools from classical game theory by embracing reinforcement learning, statistical analysis, and opponent modeling. For example, behavioral economists conclude from experimental results that people act according to levels of reasoning that form a " cognitive hierarchy " of strategies, rather than(More)
In December 2009 and November 2010, the first and second Lemonade Stand game competitions were held. In each competition, 9 teams competed, from University of Southampton, University College London, Yahoo!, Rutgers, Carnegie Mellon, Brown, Princeton, et cetera. The competition, in the spirit of Axelrod's iterated prisoner's dilemma competition, which(More)
Agent-based models are a popular way to explore the dynamics of human interactions, but rarely are these models based on empirical observations of actual human behavior. Here we exploit data collected in an experimental setting where over 150 human players played in a series of almost a hundred public goods games. First, we fit a series of deterministic(More)
This article presents a population-based cognitive hierarchy model that can be used to estimate the reasoning depth and sophistication of a collection of opponents' strategies from observed behavior in repeated games. This framework provides a compact representation of a distribution of complicated strategies by reducing them to a small number of(More)
Our project is to implement an agent that learns to play Tetris in an adversarial environment. The Tetris game was invented by Alexey Pajitnov in 1985. The version we use is a 2-dimensional game that consists of a board with a fixed size and a sequence of bocks of different sizes. Whenever the game provides a new block, the player has a certain amount of(More)
We explore the dialogue implications of a strategic voting game with a communication component and the resulting dialogue from laboratory experiments. The data reveals the role of communication in group decision making with uncertainty and personal biases. The experiment creates a conflict in human players between selfish, biased behavior and beneficial(More)
One of the challenges of multiagent decision making is that the behavior needed to maximize utility can depend on what other agents choose to do: sometimes there is no " right " answer in the absence of knowledge of how opponents will act. The Nash equilibrium is a sensible choice of behavior because it represents a mutual best response. But, even when(More)
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