Efficient agents for cliff-edge environments with a large set of decision options

This paper proposes an efficient agent for competing in Cliff Edge (CE) environments, such as sealed-bid auctions, dynamic pricing and the ultimatum game. The agent competes in one-shot CE interactions repeatedly, each time against a different human opponent, and its performance is evaluated based on all the interactions in which it participates. The agent, which learns the general pattern of the population's behavior, does not apply any examples of previous interactions in the environment , neither of other competitors nor its own. We propose a generic approach which competes in different CE environments under the same configuration, with no knowledge about the specific rules of each environment. The underlying mechanism of the proposed agent is a new meta-algorithm, Deviated Virtual Learning (DVL), which extends existing methods to efficiently cope with environments comprising a large number of optional decisions at each decision point. Experiments comparing the performance of ...

• Type: Conference
• Year: 2006
• Where: ATAL
• Authors: Ron Katz, Sarit Kraus
• Keywords: ATAL 2006, Intelligent Agents, One-shot Ce Interactions, Sealed-bid Auctions, Ultimatum Game