Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Dynamic Programming
A Trust/Honesty Model with Adaptive Strategy for Multiagent Semi-Competitive Environments
Autonomous Agents and Multi-Agent Systems
Towards agents participating in realistic multi-unit sealed-bid auctions
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Designing Peer-To-Peer Agent Auctions Using Object-Process Methodology
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Bidding strategies for realistic multi-unit sealed-bid auctions
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Risk-sensitive reinforcement learning applied to control under constraints
Journal of Artificial Intelligence Research
Bidding strategies for realistic multi-unit sealed-bid auctions
Autonomous Agents and Multi-Agent Systems
Designing bidding strategies in sequential auctions for risk averse agents
Multiagent and Grid Systems - Advances in Agent-mediated Automated Negotiations
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
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Auctions are an important means for purchasing material in the era of e-commerce. Research on auctions often studies them in isolation. In practice, however, auction agents are part of complete supply-chain management systems and have to make the same decisions as their human counterparts. To address this issue, we generalize results from auction theory in three ways. First, auction theory provides the optimal bidding function for the case where auction agents want to maximize the expected profit. Since companies are often risk-averse, we derive a closed form of the optimal bidding function for auction agents that maximize the expected utility of the profit for concave exponential utility functions. Second, auction theory often assumes that auction agents know the bidder's valuation of an auctioned item. However, the valuation depends on how the item can be used in the production process. We therefore develop theoretical results that enable us to integrate our auction agents into production-planning systems to derive the bidder's valuation automatically. Third, auction theory often assumes that the probability distribution over the competitors' valuations of the auctioned item is known. We use simulations of the combined auction- and production-planning system to obtain crude approximations of these probability distributions automatically. The resulting auction agents are part of a complete supply-chain management system and seamlessly combine ideas from auction theory, utility theory, and dynamic programming.