Automated Negotiation

Negotiation, an important facet of business, is an iterative and complex process. Depending on the nature of business and the type of transaction, negotiation criteria, duration and complexity vary widely. An intelligent software agent can help the consumers and enterprises in business negotiation. It can negotiate with humans or with other software agents to arrive at mutually agreeable terms and

Abstract We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user's preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user's preferences, but also its opponent's preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm ...

The research proposes an agent based approach, properly linked to production planning activities, able to support negotiation in catalogue based e-marketplace. In this context, production planning tools allow the creation of a link between commercialization and manufacturing activities, providing a better service for all the involved agents. At the same time negotiation tools take into account buyers’ and sellers’ identities and goals, providing better global satisfaction. A discrete event simulation environment has been properly developed. The simulation results show how to obtain an “e-marketplace mapping” in order to evaluate the real value added in a dynamic environment. This information can support Small and Medium Enterprises in e-marketplace adoption decision making process.

We are concerned with forms of interaction in which multiple users, with differing agendas and interests, may realise opportunities for useful synchronisation of their activities. We present a framework in which intelligent software agents act as semi-autonomous intermediaries among nomadic users. Agents capture and process information about situations (specifically about the environment, users and their activities) in order to jointly find and negotiate opportunities for coordinating the activities of their respective users. The interaction is structured using a negotiation protocol that exploits a hierarchical representation of tasks and goals.

The proposed protocol has been designed to support a flexible formation of request-based virtual organizations with an emphasis on reflecting the conditions of real competitive environments. It supports automated or semi-automated negotiations mainly in the creation part of a virtual organization life cycle and accounts for a use of service level agreements. The protocol consists of three phases: (i) potential

When studying extensive-form games it is commonly assumed that players make their decisions individually. One usually does not allow the possibility for the players to negotiate their respective strategies and formally commit themselves to future moves. As a consequence, many non-zero-sum games have been shown to have equilibrium outcomes that are suboptimal and arguably counter-intuitive. For this reason we feel there is a need to explore a new line of research in which game-playing agents are allowed to negotiate binding agreements before they make their moves. We analyze what happens under such assumptions and define a new equilibrium solution concept to capture this. We show that this new solution concept indeed yields solutions that are more efficient and, in a sense, closer to what one would expect in the real world. Furthermore, we demonstrate that our ideas are not only theoretical in nature , but can also be implemented on bounded rational agents, with a number of experiments conducted with a new algorithm that combines techniques from Automated Negotiations, (Algorithmic) Game Theory, and General Game Playing. Our algorithm, which we call Monte Carlo Negotiation Search, is an adaptation of Monte Carlo Tree Search that equips the agent with the ability to negotiate. It is completely domain-independent in the sense that it is not tailored to any specific game. It can be applied to any non-zero-sum game, provided that its rules are described in Game Description Language. We show with several experiments that it strongly outperforms non-negotiating players, and that it closely approximates the theoretically optimal outcomes, as defined by our new solution concept.

In multi-agent systems (MAS), negotiation provides a powerful metaphor for automating the allocation and reallocation of resources. Methods for automated negotiation in MAS include auction-based protocols and alternating offer bargain- ing protocols. Recently, argumentation-based negotiation has been accepted as a promising alternative to such approaches. Interest-based negotiation (IBN) is a form of argumentation-based negotiation in which agents exchange (1) informa-

The proposed protocol has been designed to support the flexible formation of Request-Based Virtual Organisations (RBVOs) with an emphasis on reflecting the conditions of real competitive environments. It supports automated or semi-automated negotiations mainly in the creation part of a Virtual Organisation (VO) life cycle and it accounts for the use of Service Level Agreements (SLAs). The protocol consists of

This book constitutes the refereed proceedings of the 11th International Workshop on Cooperative Information Agents, CIA 2007, held in Delft, The Netherlands, September 2007. The 19 revised full papers presented together with four invited papers were carefully reviewed and selected from 38 submissions. The papers are organized in topical sections on information search and processing, applications, rational cooperation, interaction and cooperation and trust.

In this paper we study multi issue alternating-offers bargaining in a perfect information finite horizon setting, we determine the pertinent subgame perfect equilibrium, and we provide an algorithm to compute it. The equilibrium is determined by making a novel use of backward induction together with convex programming techniques in multi issue settings. We show that the agents reach an agreement immediately and that such an agreement is Pareto efficient. Furthermore, we prove that, when the multi issue utility functions are linear, the problem of computing the equilibrium is tractable and the related complexity is polynomial with the number of issues and linear with the deadline of bargaining.

The automation of B2B processes requires a high level of interoperability between potentially disparate systems. We model such systems using software agents (representing enterprises), which interact using specific protocols. When considering open environments, interoperability problems are even more challenging. Addressing business automation as a task that intends to align businesses through a tight integration of processes may not be desirable, because business relationships may be temporary and dynamic. Furthermore, ...

Agent-mediated automated negotiation is a key form of interaction in the e-commerce environment. Agents reach an agreement through an iterative process of making offers. However, agents are prone to conceal their private negotiation information, which decreases the efficiency of negotiation. In this paper, an ensemble learning-based negotiation method is proposed. The new method labels the proposals automatically by mining the implicit information in negotiation history data. Then, the labeled proposals become the training samples of the ensemble learning algorithm, which generates the estimation of the opponent's utility function. At last, based on the utility function of both sides, a win-win negotiation counter-proposal is generated through a particle swarm optimization algorithm. The experimental results indicate the benefits and efficiency of the proposed method.