Reasoning and negotiating with complex preferences using CP-nets

Lecture Notes in Computer Science, 2014

Conflicts of interest occur in various aspects of our daily life and we often come to an understanding by negotiating our way through these conflicts. This paper presents a simple interactive negotiation approach to resolve certain conflicts that involve multiple issues. The focus is on mediation to facilitate a solution based on alternating offers over a finite-time bargaining game. The mediator explores the possibilities and proposes a jointly optimal negotiation text for all the players participating in the negotiation process, based on their conditional preference networks (CP-nets). Each individual player then makes a decision to accept or reject the proposal based on their utility CP-nets. If any player rejects, the mediator offers another negotiation text and the process goes on until an agreement is achieved or some time limit is reached. Two algorithms are developed with regard to the players as well as the mediator, and a daily-life situation is investigated based on them. A historically important negotiation event has also been investigated using this model.

Negotiation and Argumentation in Multi-Agent Systems, 2014

In this chapter we present an application and framework to automate multi-issue bilateral negotiation in e-markets. We address several challenges of a typical negotiation in an online marketplace, such as (i) how to elicit preferences from users; (ii) how to formally represent preferences that at the same time allow human users to express both qualitative and quantitative preferences; (iii) how to compute agreements which are mutual beneficial for both buyer and seller, i.e., outcome enjoying economics properties as Pareto-efficiency. The issue of preference elicitation is addressed with the help of an easy-touse graphical interface hiding all the technicalities of the underlying framework. Preferences are then mapped to a logic language, that allows one to express preferences on both numerical and non-numerical features. We build a utility function on top of this logic language in order to permit the representation of relative importance among preferences, to evaluate the possible agreements and finally choose the one(s) enjoying the Pareto-efficiency property. F. Lopes and H. Coelho (Eds.), Negotiation and Argumentation in MAS 1 All rights reserved −

Autonomous Agents and Multi-agent Systems, 2007

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 cooperation with, among others, Dutch Telecom KPN. The approach can be characterized as cooperative one-to-one multi-criteria negotiation in which the privacy of both parties is protected as much as desired. We model a mechanism in which agents are able to use any amount of incomplete preference information revealed by the negotiation partner in order to improve the efficiency of the reached agreements. Moreover, we show that the outcome of such a negotiation can be further improved by incorporating a “guessing” heuristic, by which an agent uses the history of the opponent’s bids to predict his preferences. Experimental evaluation shows that the combination of these two strategies leads to agreement points close to or on the Pareto-efficient frontier. The main original contribution of this paper is that it shows that it is possible for parties in a cooperative negotiation to reveal only a limited amount of preference information to each other, but still obtain significant joint gains in the outcome.

2001

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 characterized as co-operative one-to-one multi-criteria negotiation in which the privacy of both parties is protected as much as desired. We model a mechanism in which agents are able to use any amount of incomplete preference information revealed by the negotiation partner in order to improve the efficiency of the reached agreements. Moreover, we show that the outcome of such a negotiation can be further improved by incorporating a "guessing " heuristic, by which an agent uses the history of the opponent's bids to predict his preferences. Experimental evaluation shows that the combination of these two strategies leads to agreement points close to or on the Paretoefficient frontier. The main original...

Studies in Computational Intelligence, 2010

Automated negotiation has become increasingly important and pervasive since the advent of e-Business. It frees people from tedious interactions, improves the efficiency of e-business and ensures the accuracy of complex service composition. However, there are limitations of the existing negotiation models. Firstly, the majority of existing negotiation models are "price" bargain type of negotiation. It does not consider the reasons lead to the bargain position. Secondly, a few interest based negotiation models proposed in recent years are able to consider the underlying reasons of the counter party's position, therefore, have more chance to reach an agreement. However, they focus on individual's alternative solution seeking. None of these models promote the most productive human negotiation approach, especially in the global economic context, to constructively cooperate and seek for possible win-win situations. In an e-business environment, it would be more powerful if new services could be built on multiple parties' existing services to form a cooperative solution. This paper proposes a negotiation model to enable negotiation parties to exchange preferences and knowledge, develop optimal cooperative solutions for mutual benefits. It is a cooperative-competitive win-win strategy.

Lecture Notes in Computer Science, 2005

In this paper we present research works on non-intuitive and low-efficient negotiations between agents in agent based system. We find recommendation techniques as a suitable method of making negotiation smarter and more efficient. We have introduced into the negotiation thread the improvements in the form of hybrid recommendation. Hybrid recommendation is composed of three basic elements: demographic, collaborative and contentbased. On the base of a negotiation algorithm we have studied how recommendation methods can improve the whole process of finding mutually acceptable agreements between agents. The proposed methodology is presented using a travel agency and its client negotiation.

Studies in Computational Intelligence, 2012

In online and dynamic e-commerce environments, it is beneficial for parties to consider each other's preferences in carrying out transactions. This is especially important when parties are negotiating, since considering preferences will lead to faster closing of deals. However, in general may not be possible to know other participants' preferences. Thus, learning others' preferences from the bids exchanged during the negotiation becomes an important task. To achieve this, the producer agent may need to make assumptions about the consumer's preferences and even its negotiation strategy. Nevertheless, these assumptions may become inconsistent with a variety of preference representations. Therefore, it is more desired to develop a learning algorithm, which is independent from the participants' preference representations and negotiation strategies. This study presents a negotiation framework in which the producer agent learns an approximate model of the consumer's preferences regardless of the consumer's preference representation. For this purpose, we study our previously proposed inductive learning algorithm, namely Revisable Candidate Elimination Algorithm (RCEA). Our experimental results show that a producer agent can learn the consumer's preferences via RCEA when the consumer represents its preferences using constraints or CP-nets. Further, in both cases, learning speeds up the negotiation considerably.

2001

The world of E-comn~rce presents ample opportunity to fully utilize the capability of intelligent agents. The highly dynamic, fast-moving and information-rich environment can often be overwhelming for the human participant. Agents can intelligently assist users by mimicking human behaviour and adapting themselves to their client's specification. This paper explores the viability of running a society of agents in a simple marketplace to negotiate on behalf of human clients. The type of agent chosen for this role is a plan-based agent. This architecture offers the flexibility and reasoning power required to maximize the possibilities presented in the environment chosen.

Multiagent and Grid Systems, 2017

The ubiquity of electronic commerce has resulted in considerable interest in automating the mechanisms it employs. Ideally, we would like to only leave closing a deal to the human user, and have software negotiate price. This paper propose a negotiation strategy that focuses on 1) beginning the negotiation process at a realistic and competitive price, 2) encouraging the continuation of a round of negotiations once it has begun, and 3) keeping the computation and communication load low so that the bulk of marketplace resources are spent on the negotiation process itself. We offer comparison with respect to the negotiated price, negotiation time and communication overhead against Red Agent and show favorable results. This work also presents the design of a marketplace where agents would meet and negotiate through a protocol, read product information from a central ontology, and be able to fetch negotiated prices for a product from the central history database. The work therefore demonstrates feasibility for a negotiation support system comprising both the negotiation strategy and a negotiation infrastructure.

2004

This paper presents a model for integrative, one-to-one negotiation in which the values across multiple attributes are negotiated simultaneously. We model a mechanism in which agents are able to use any amount of incomplete preference information revealed by the negotiation partner in order to improve the efficiency of the reached agreements. Moreover, we show that the outcome of such a negotiation can be further improved by incorporating a "guessing" heuristic, by which an agent uses the history of the opponent's bids to predict his preferences. Experimental evaluation shows that the combination of these two strategies leads to agreement points close to or on the Pareto-efficient frontier. The main original contribution of this paper is that it shows that it is possible for parties in a cooperative negotiation to reveal only a limited amount of preference information to each other, but still obtain significant joint gains in the outcome.