Combinatorial auctions for supply chain formation
Proceedings of the 2nd ACM conference on Electronic commerce
An auction-based method for decentralized train scheduling
Proceedings of the fifth international conference on Autonomous agents
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Risk and expectations in a-priori time allocation in multi-agent contracting
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Problem difficulty for tabu search in job-shop scheduling
Artificial Intelligence
A Combinatorial Auction for Collaborative Planning
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Agent-mediated electronic commerce: a survey
The Knowledge Engineering Review
Asking the right question: Risk and expectation in multiagent contracting
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A winner determination algorithm for auction-based decentralized scheduling
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Optimal combinatorial electricity markets
Web Intelligence and Agent Systems
Constraint-based winner determination for auction-based scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In our previous research we suggested an approach to maximizing agents preferences over schedules of multiple tasks with temporal and precedence constraints. The proposed approach is based on Expected Utility Theory. In this paper we address two mutually dependent questions: (a) what are the properties of the problem domain that can facilitate efficient maximization algorithms, and (b) what criteria determine attractiveness of one or another potential solution to the agent. We discuss different ways of exploring the problem domain. We show that naive optimization approaches often fail to find solutions for risk-averse agents and propose ways of using properties of the domain to improve upon naive approaches.