Strategic negotiation in multiagent environments
Strategic negotiation in multiagent environments
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Optimal agendas for multi-issue negotiation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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Negotiating over small bundles of resources
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Allocation of indivisible goods: a general model and some complexity results
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Propositional-logic approach to one-shot multi issue bilateral negotiation
ACM SIGecom Exchanges
Playing for Real: A Text on Game Theory
Playing for Real: A Text on Game Theory
A logic-based framework to compute Pareto agreements in one-shot bilateral negotiation
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
A computational model of logic-based negotiation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A scheme for integrating concrete domains into concept languages
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
A knowledge-based framework for complex, proactive and service-oriented e-negotiation systems
Electronic Commerce Research
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We present a novel logic-based approach to automate multi-issue bilateral negotiation in e-marketplaces. In such frameworks issues to negotiate on can be multiple, interrelated, and may not be fixed in advance. We use logic to model relations among issues and to allow agents express their preferences on them. In particular, we introduce the logic P(N), a fuzzy propositional logic extended with concrete domains in order to handle numerical, as well as non numerical features, and to deal with vagueness in buyer/seller preferences. Hence, agents can express preferences as e.g., I am searching for a passenger car costing about 25000€ yet if the car has a GPS system and more than two-year warranty I can spend up to 28000€. We illustrate the theoretical framework, the logical language, the protocol we adopt and show that using a mediator with a proactive behavior we can compute Pareto-efficient agreements.