15,298 research outputs found
Using Similarity Criteria to Make Negotiation Trade-Offs
This paper addresses the issues involved in software agents making trade-offs during automated negotiations in which they have information uncertainty and resource limitations. In particular, the importance of being able to make trade-offs in real-world applications is highlighted and a novel algorithm for performing trade-offs for multi-dimensional goods is developed. The algorithm uses the notion of fuzzy similarity in order to find negotiation solutions that are beneficial to both parties. Empirical results indicate the benefits and effectiveness of the trade-off algorithm in a range of negotiation situations
Efficient Methods for Automated Multi-Issue Negotiation: Negotiating over a Two-Part Tariff
In this article, we consider the novel approach of a seller and customer negotiating bilaterally about a two-part tariff, using autonomous software agents. An advantage of this approach is that win-win opportunities can be generated while keeping the problem of preference elicitation as simple as possible. We develop bargaining strategies that software agents can use to conduct the actual bilateral negotiation on behalf of their owners. We present a decomposition of bargaining strategies into concession strategies and Pareto-efficient-search methods: Concession and Pareto-search strategies focus on the conceding and win-win aspect of bargaining, respectively. An important technical contribution of this article lies in the development of two Pareto-search methods. Computer experiments show, for various concession strategies, that the respective use of these two Pareto-search methods by the two negotiators results in very efficient bargaining outcomes while negotiators concede the amount specified by their concession strategy
Automated Negotiation for Provisioning Virtual Private Networks Using FIPA-Compliant Agents
This paper describes the design and implementation of negotiating agents for the task of provisioning virtual private networks. The agents and their interactions comply with the FIPA specification and they are implemented using the FIPA-OS agent framework. Particular attention is focused on the design and implementation of the negotiation algorithms
An Evolutionary Learning Approach for Adaptive Negotiation Agents
Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive 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 real-world applications
Generating Pareto-Optimal Offers in Bilateral Automated Negotiation with One-Side Uncertain Importance Weights
Pareto efficiency is a seminal condition in the bargaining problem which leads autonomous agents to a Nash-equilibrium. This paper investigates the problem of the generating Pareto-optimal offers in bilateral multi-issues negotiation where an agent has incomplete information and the other one has perfect information. To this end, at first, the bilateral negotiation is modeled by split the pie game and alternating-offer protocol. Then, the properties of the Pareto-optimal offers are investigated. Finally, based on properties of the Pareto-optimal offers, an algorithmic solution for generating near-optimal offers with incomplete information is presented. The agent with incomplete information generates near-optimal offers in O(n Ćog n). The results indicate that, in the early rounds of the negotiation, the agent with incomplete information can generate near-optimal offers, but as time passes the agent can learn its opponents preferences and generate Pareto-optimal offers. The empirical analysis also indicates that the proposed algorithm outperform the smart random trade-offs (SRT) algorithm
Negotiating over Bundles and Prices Using Aggregate Knowledge
Combining two or more items and selling them as one good, a practice called
bundling, can be a very effective strategy for reducing the costs of producing,
marketing, and selling goods. In this paper, we consider a form of multi-issue
negotiation where a shop negotiates both the contents and the price of bundles
of goods with his customers. We present some key insights about, as well as a
technique for, locating mutually beneficial alternatives to the bundle
currently under negotiation. The essence of our approach lies in combining
historical sales data, condensed into aggregate knowledge, with current data
about the ongoing negotiation process, to exploit these insights. In
particular, when negotiating a given bundle of goods with a customer, the shop
analyzes the sequence of the customer's offers to determine the progress in the
negotiation process. In addition, it uses aggregate knowledge concerning
customers' valuations of goods in general. We show how the shop can use these
two sources of data to locate promising alternatives to the current bundle.
When the current negotiation's progress slows down, the shop may suggest the
most promising of those alternatives and, depending on the customer's response,
continue negotiating about the alternative bundle, or propose another
alternative. Extensive computer simulation experiments show that our approach
increases the speed with which deals are reached, as well as the number and
quality of the deals reached, as compared to a benchmark. In addition, we show
that the performance of our system is robust to a variety of changes in the
negotiation strategies employed by the customers.Comment: 15 pages, 7 eps figures, Springer llncs documentclass. Extended
version of the paper published in "E-Commerce and Web Technologies," Kurt
Bauknecht, Martin Bichler and Birgit Pr\"{o}ll (eds.). Springer Lecture Notes
in Computer Science, Volume 3182, Berlin: Springer, p. 218--22
The significance of bidding, accepting and opponent modeling in automated negotiation
Given the growing interest in automated negotiation, the search for effective strategies has produced a variety of different negotiation agents. Despite their diversity, there is a common structure to their design. A negotiation agent comprises three key components: the bidding strategy, the opponent model and the acceptance criteria. We show that this three-component view of a negotiating architecture not only provides a useful basis for developing such agents but also provides a useful analytical tool. By combining these components in varying ways, we are able to demonstrate the contribution of each component to the overall negotiation result, and thus determine the key contributing components. Moreover, we are able to study the interaction between components and present detailed interaction effects. Furthermore, we find that the bidding strategy in particular is of critical importance to the negotiator's success and far exceeds the importance of opponent preference modeling techniques. Our results contribute to the shaping of a research agenda for negotiating agent design by providing guidelines on how agent developers can spend their time most effectively
Online Learning of Aggregate Knowledge about Non-linear Preferences Applied to Negotiating Prices and Bundles
In this paper, we consider a form of multi-issue negotiation where a shop
negotiates both the contents and the price of bundles of goods with his
customers. We present some key insights about, as well as a procedure for,
locating mutually beneficial alternatives to the bundle currently under
negotiation. The essence of our approach lies in combining aggregate
(anonymous) knowledge of customer preferences with current data about the
ongoing negotiation process. The developed procedure either works with already
obtained aggregate knowledge or, in the absence of such knowledge, learns the
relevant information online. We conduct computer experiments with simulated
customers that have_nonlinear_ preferences. We show how, for various types of
customers, with distinct negotiation heuristics, our procedure (with and
without the necessary aggregate knowledge) increases the speed with which deals
are reached, as well as the number and the Pareto efficiency of the deals
reached compared to a benchmark.Comment: 10 pages, 5 eps figures, ACM Proceedings documentclass, Published in
"Proc. 6th Int'l Conf. on Electronic Commerce ICEC04, Delft, The
Netherlands," M. Janssen, H. Sol, R. Wagenaar (eds.). ACM Pres
A community of agents as a tool to optimize industrial districts logistics
The aim of this paper is to find an optimal solution to operational planning of freight transportation in
an industrial district. We propose a system architecture that drives agents â the industrial district firms - to
cooperate in logistic field, to minimize transport and environmental costs. The idea is to achieve logistics
optimization setting up a community made of district enterprises, preserving a satisfactory level of system
efficiency and fairness. We address the situation in which a virtual coordinator helps the agents to reach
an agreement. The objectives are: maximizing customers satisfaction, and minimizing the number of
trucks needed. A fuzzy clustering (FCM), two Fuzzy Inference System (FIS) combined with a Genetic
Algorithm (GA), and a greedy algorithm are thus proposed to achieve these objectives, and eventually an
algorithm to solve the Travelling Salesman Problem is also used. The proposed framework can be used to
provide real time solutions to logistics management problems, and negative environmental impacts
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