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This paper investigates the influence of access to (and use of) different sources of information about potential task- partners behavior on the performance of self-interested agents that are operating in an open environment. The paper focuses on scenarios in which agents must choose partners to accomplish tasks and on task settings in which there is a small group of potential partners, including both...
This paper proposes an efficient agent for competing in simultaneous substitutional cliff-edge (SCE) environments, which include simultaneous auctions and multi-player Ultimatum-Games. The agent competes in one-shot interactions repeatedly, each time against different human opponents, and its performance is evaluated based on all the interactions in which it participates. It learns the general pattern...
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