A survey of automated negotiation: Human factor, learning, and application

Abstract

The burgeoning field of automated negotiation systems represents a transformative approach to resolving conflicts and allocating resources with enhanced efficiency. This paper presents a thorough survey of this discipline, emphasising the implications of human factors, the application of machine learning techniques, and the real-world deployments of these systems. In traditional manual negotiation, various challenges emerge, including limited negotiation skills, power asymmetries, personality disparities, and cultural influences. Automated negotiation systems can offer solutions to these challenges through their round-the-clock availability, the ability to negotiate without emotional bias, efficient information access, and seamless integration of cultural contexts. This comprehensive survey delves into the intricacies of human–computer negotiation, shedding light on the impact of emotional cues, cultural diversity, and the subtleties of language. Furthermore, the study reviews the incorporation of machine learning models that facilitate the adaptation of negotiation strategies. The paper also discusses the application of fuzzy set theory and fuzzy constraint methods within the scope of automated negotiation, providing a valuable addition to the existing literature. Real-world deployment of these systems in domains e.g., e-commerce, conflict resolution, and multi-agent systems is also examined. By providing a broad overview of automated negotiation, this survey acknowledges the vital role of human factors in negotiation processes, underscores the value of intelligent and adaptive negotiation techniques and offers valuable insights into the practical applications of these systems in various real-world contexts.