Communications of the ACM
Bundling Information Goods: Pricing, Profits, and Efficiency
Management Science
Competitive bundling of categorized information goods
Proceedings of the 2nd ACM conference on Electronic commerce
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Intelligent Sales Assistant for Configurable Products
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Pricing Bundled Information Goods
WECWIS '02 Proceedings of the Fourth IEEE International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS'02)
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Modeling complex multi-issue negotiations using utility graphs
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A scalable method for online learning of non-linear preferences based on anonymous negotiation data
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A fast method for learning non-linear preferences online using anonymous negotiation data
TADA/AMEC'06 Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets
Eliminating issue dependencies in complex negotiation domains
Multiagent and Grid Systems - Advances in Agent-mediated Automated Negotiations
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Hi-index | 0.00 |
In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process.The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have non-linear preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmark.