Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
The RAPPID Project: Symbiosis between Industrial Requirements and MAS Research
Autonomous Agents and Multi-Agent Systems
Agent-Based Engineering, the Web, and Intelligence
IEEE Expert: Intelligent Systems and Their Applications
Performance-Based Incentives in a Dynamic Principal-Agent Model
Manufacturing & Service Operations Management
A collaborative design process model in the sociotechnical engineering design framework
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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Modularization of design and the decreasing time-to-market of new products have placed increased emphasis on Collaborative Product Development (CPD) for success in the global market. We recognize the need for a general framework for design negotiation during CPD. In this research, we propose a principal-agent model for negotiation. The model incorporates a penalty induced negotiation (PIN) mechanism that decomposes the collaborative design into a sequence of decision making stages. The PIN mechanism introduces two types of agents: a principal agent that monitors and controls the iterative negotiation process, and design agents that represent the participants in the collaboration. The principal agent uses a multi-attribute utility based approach to penalize design agents that violate global constraints. The design agents are rational and react to penalties imposed on them by compromising their design in order to satisfy the violated global constraints. We illustrate the use of the mechanism on a pressure vessel design example.