Multistage negotiation in distributed planning
Distributed Artificial Intelligence
Coordination of Distributed Problem Solvers
Coordination of Distributed Problem Solvers
A framework for problem-solving in a distributed processing environment.
A framework for problem-solving in a distributed processing environment.
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
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Eighteenth national conference on Artificial intelligence
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Anyone but him: The complexity of precluding an alternative
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Incentive compatible multiagent constraint optimization
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Voting in cooperative information agent scenarios: use and abuse
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A budget-balanced, incentive-compatible scheme for social choice
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Incentive engineering for Boolean games
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Studies in computational aspects of voting: open problems of downey and fellows
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The complexity of losing voters
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Designing social choice mechanisms using machine learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Generalized scoring rules: a framework that reconciles Borda and Condorcet
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Parameterized enumeration of (locally-) optimal aggregations
WADS'13 Proceedings of the 13th international conference on Algorithms and Data Structures
Control complexity of schulze voting
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
An adaptive approach for decision making tactics in automated negotiation
Applied Intelligence
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When autonomous agents attempt to coordinate action, it is often necessary that they reach some kind of consensus. Reaching such a consensus has traditionally been dealt with in the Distributed Artificial Intelligence literature via the mechanism of negotiation. Another alternative is to have agents bypass negotiation by using a voting mechanism; each agent expresses its preferences, and a group choice mechanism is used to select the result. Some choice mechanisms are better than others, and ideally we would like one that. cannot be manipulated by an untruthful agent. One such non-manipulable choice mechanism is the Clarke tax [Clarke, 1971]. Though theoretically attractive, the Clarke tax presents a number of difficulties when one attempts to use it in a practical implementation. This paper examines how the Clarke tax could be used as an effective "preference revealer" in the domain of automated agents, reducing the need for explicit negotiation.