A component-based generic agent architecture for multi-attribute (integrative) negotiation is introduced and its application is described in a prototype system for negotiation about cars, developed in co-operation with, among others, Dutch Telecom KPN. The approach can be characterised as co-operative one-toone multi-criteria negotiation in which the privacy of both parties is protected as much as possible. 1

Summary. This chapter discusses coordination from a commitment basis. Typically, commitments are established via a process of negotiation between the parties— the debtor and creditor—involved in the commitment. We define obligations to be those commitments, sometimes termed norms or social commitments, without a clearly identifiable creditor. The establishment of a commitment occurs in response to the adoption of a goal or the acceptance and performance of a task. Using a service-oriented computing (SOC) context, we describe an efficient negotiation process for establishing commitments. We then show how commitments and obligations can be used to monitor and control the aggregate behavior of a group of agents to yield coordinated progress towards the agents ’ overall objective. 1

This paper presents an agent strategy for complex bilateral negotiations over many issues with inter-dependent valuations. We use ideas inspired by graph theory and probabilistic influence networks to derive efficient heuristics for negotiations about multiple issues. Experimental results show — under relatively weak assumptions with respect to the structure of the utility functions – that the developed approach leads to Pareto-efficient outcomes. Moreover, Pareto-efficiency can be reached with few negotiation steps, because we explicitly model and utilize the underlying graphical structure of complex utility functions. Consequently, our approach is applicable to domains where reaching an efficient outcome in a limited amount of time is important. Furthermore, unlike other solutions for high-dimensional negotiations, the proposed approach does not require a mediator.

This paper presents a decentralized model that allows self-interested agents to reach “win-win ” agreements in a multi-attribute negotiation. The model is based on an alternating-offer protocol. In each period, the proposing agent is allowed to make a limited number of offers. The responding agent can select the best out of these offers. In the case of rejection, agents exchange their roles and the negotiation proceeds to the next period. To make counteroffers, an agent first uses the heuristic of choosing the offer on an indifference (or “iso-utility”) curve/surface that is closest to the best offer made by the opponent in the previous period, and then taking this offer as the seed, chooses several other offers randomly in a specified neighborhood of this seed offer. Experimental results show that this model induces agents to reach near Pareto optimal agreements in general situations where agents have complex preferences on the attributes and incomplete information. This model does not require the presence of a mediator. Keywords: Multi-attribute negotiation, Pareto optimality, Win-win, Rational preference, Incomplete information, Self-interested agents

This paper presents the demonstration of a software system for integrative negotiation. The agents in this system conduct one-to-one negotiations, in which the values across multiple attributes are negotiated on simultaneously. It is demonstrated how the system supports both automated negotiation (i.e., conducted by a software agent) and human negotiation (where humans specify their bids). Furthermore, it is shown how, compared to fully closed negotiation, the efficiency of the reached agreements may be improved, either by using incomplete preference information revealed by the negotiation partner or by incorporating a heuristic, through which an agent uses the history of the opponent's bids in order to guess his preferences.

Abstract. This paper presents a System for Analysis of Multi-Issue Negotiation (SAMIN). The agents in this system conduct one-to-one negotiations, in which the values across multiple issues are negotiated on simultaneously. It is demonstrated how the system supports both automated negotiation (i.e., conducted by a software agent) and human negotiation (where humans specify their bids). To analyse such negotiation processes, the user can enter any formal property deemed useful into the system and use the system to automatically check this property in given negotiation traces. Furthermore, it is shown how, compared to fully closed negotiation, the efficiency of the reached agreements may be improved, either by using incomplete preference information revealed by the negotiation partner or by incorporating a heuristic, through which an agent uses the history of the opponent's bids in order to guess his preferences. 1.