Adaptive negotiation agents for e-business

Abstract

Negotiation has been identified as one of the key steps in Business-to-Business (B2B) transaction models. However, developing effective and efficient negotiation mechanisms for e-Business is quite challenging since negotiations in such a context are characterized by combinatorial complex negotiation spaces, tough deadlines, incomplete information about the opponents, and volatile negotiator preferences. Classical negotiation models are not able to offer a satisfactory solution to address all these issues. This paper illustrates our adaptive negotiation agents which are underpinned by a robust evolutionary learning mechanism to deal with complex and dynamic negotiation situations often encountered in e-Business applications. Our experimental results show that the proposed evolutionary negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for e-Business.

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

“…Faratin et al show that applying this approach in a negotiation agent can result in mutually beneficial outcomes with a relatively higher gain. The heuristic is implemented in [111], and Lau et al use a similar approach combined with a genetic algorithm [112,114].…”

A Review of Learning Methods Enhanced in Strategies of Negotiating Agents

“…On the contrary, in cases where GAs were applied during the current discourse, populations of chromosomes were used to represent the population of feasible offers. Such application can be found in (Lau, 2005) where the fitness of each offer is measured with respect to its distance from the most preferred offer, the distance from the opponents' previous offer and the time pressure. In each round the offers considered fit by the agent may change.…”

Or Best Offer: A Privacy Policy Negotiation Protocol

“…Pareto-optimality, or Pareto-efficiency is a property of some game and negotiation end-states. It is often used as an indication that the benefits of successful negotiations are balanced for both parties [2,9,4]. For a state to be Paretooptimal, it must be the case that there is no other state that is better for all parties in the negotiation, or better for at least one party and not worse for all the others.…”