Browsing by Author "Damer, Steven"

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    A Minimally Constrained Environment for the Study of Cooperation
    (2008-04-24) Damer, Steven; Gini, Maria
    We describe a simple environment to study cooperation between two agents and a method of achieving cooperation in that environment. The environment consists of randomly generated normal form games with uniformly distributed payoffs. Agents play multiple games against each other, each game drawn independently from the random distribution. This environment provides a good model of the difficulties of cooperating in an ever changing world. Tit-for-Tat cannot be used because moves are not labeled as "cooperate" or "defect", fictitious play cannot be used because the agent never sees the same game twice, and approaches suitable for stochastic games cannot be used because the set of states is not finite. Our agent identifies cooperative moves by assigning an attitude to its opponent and to itself. The attitude determines how much a player values its opponents payoff, i.e how much the player is willing to deviate from strictly self-interested behavior. To cooperate, our agent estimates the attitude of its opponent by observing its moves and reciprocates by setting its own attitude accordingly. We show how the opponent's attitude can be estimated using a particle filter, even when the opponent is changing its attitude.
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    Component-based design for a trading agent
    (2004-05-04) Collins, John; Agovic, Amrudin; Damer, Steven; Gini, Maria
    MinneTAC is an agent designed to compete in the Supply-Chain Trading Agent Competition. It is also designed to support the needs of a group of researchers, each of whom is interested in different decision problems related to the competition scenario. The design of MinneTAC breaks out each basic behavior into a separate, configurable component. Dependencies between components are almost non-existent. The agent itself is defined as a set of "roles", and a working agent is one for which a component is supplied for each role. This allows each user to focus on a single problem and work independently, and it allows multiple user to tackle the same problem in different ways. A working MinneTAC agent is completely defined by a set of configuration files that map the desired roles to the code that implements them, and that set parameters for the components. This paper describes the design of MinneTAC and evaluates its effectiveness in support of our research agenda, and in its competitiveness in the TAC-SCM game environment.
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    Cooperation in Games
    (2019-05) Damer, Steven
    This dissertation explores several problems related to social behavior, which is a complex and difficult problem. In this dissertation we describe ways to solve problems for agents interacting with opponents, specifically (1) identifying cooperative strategies,(2) acting on fallible predictions, and (3) determining how much to compromise with the opponent. In a multi-agent environment an agent’s interactions with its opponent can significantly affect its performance. However, it is not always possible for the agent to fully model the behavior of the opponent and compute a best response. We present three algorithms for agents to use when interacting with an opponent too complex to be modelled. An agent which wishes to cooperate with its opponent must first identify what strategy constitutes a cooperative action. We address the problem of identifying cooperative strategies in repeated randomly generated games by modelling an agent’s intentions with a real number, its attitude, which is used to produce a modified game; the Nash equilibria of the modified game implement the strategies described by the intentions used to generate the modified game. We demonstrate how these values can be learned, and show how they can be used to achieve cooperation through reciprocation in repeated randomly generated normal form games. Next, an agent which has formed a prediction of opponent behavior which maybe incorrect needs to be able to take advantage of that prediction without adopting a strategy which is overly vulnerable to exploitation. We have developed Restricted Stackelberg Response with Safety (RSRS), an algorithm which can produce a strategy to respond to a prediction while balancing the priorities of performance against the prediction, worst-case performance, and performance against a best-responding opponent. By balancing those concerns appropriately the agent can perform well against an opponent which it cannot reliably predict. Finally we look at how an agent can manipulate an opponent to choose actions which benefit the agent. This problem is often complicated by the difficulty of analyzing the game the agent is playing. To address this issue, we begin by developing a new game, the Gift Exchange game, which is trivial to analyze; the only question is how the opponent will react. We develop a variety of strategies the agent can use when playing the game, and explore how the best strategy is affected by the agent’s discount factor and prior over opponents.
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    Design and Analysis of the MinneTAC-03 Supply-Chain Trading Agent
    (2004-04-26) Ketter, Wolfgang; Kryzhnyaya, Elena; Damer, Steven; McMillen, Colin; Agovic, Amrudin; Collins, John; Gini, Maria
    MinneTAC is an agent designed to compete in the Supply-Chain Trading Agent Competition. It is also designed to support the needs of a group of researchers, each of whom is interested in different decision problems related to the competition scenario. The design of MinneTAC breaks outeach basic behavior into a separate, configurable component. Dependencies between components are almost non-existent. This design allows each user to focus on a single problem and work independently, and it allows multiple user to tackle the same problem in different ways. This paper describes the design of MinneTAC and evaluates its effectiveness in support of our research agenda, and in its competitiveness in the TAC-SCM game environment. We also describe two sales strategies used by MinneTAC. Both strategies estimate, as the game progresses, the probability of receiving a customer order for different prices and compute the expected profit. Offers are made to maximize the expected profit on each order. The maindifference between the two strategies is in how the probability of receiving an order and the offer prices are computed. The first strategy works well in high-demand games, the second was developed to improve performance in low-demand games. We empirically analyze the effect of the discount given by suppliers on orders received the firstday of the game, and we show that in high-demand games there is a strong correlation between the offers an agent receives from suppliers on the first day of the game and the agent's performance in the game.