Negotiation In Multi-agent System Research Articles

Agents’ Uncertainty in Argumentation-based Negotiation: Classification and Implementation

In this paper, we tackle the problem of uncertainty in argumentation-based agent negotiation by analyzing the different situations of this uncertainty. In particular, we analyze two types of agents’ uncertainty called Type I and Type II. Uncertainty Type I is the agent's uncertainty about selecting the right moves during the dialogue. Uncertainty Type II is the agent's uncertainty that the selected move will be accepted by the addressee. More precisely, we discuss these uncertainties in two cases based on the different classes that arguments can belong to. In addition to the theoretical analysis of arguments uncertainty, we implemented the proposed approach by applying it on a concrete case study (Buyer/Seller scenario). The obtained empirical results confirm the effectiveness of using our uncertainty-aware techniques and show that our negotiating agents outperform others (which do not use such techniques). We believe that such analysis will advance the research in the area of argumentation-based negotiation in multiagent systems and contribute to the automation of the agents’ negotiation.

A formal analysis of interest-based negotiation

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 bargaining 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) information about their underlying goals; and (2) alternative ways to achieve these goals. However, the usefulness of IBN has been mostly established in the literature by appeal to intuition or by use of specific examples. In this paper, we propose a new formal model for reasoning about interest-based negotiation protocols. We demonstrate the usefulness of this framework by defining and analysing two different IBN protocols. In particular, we characterise conditions that guarantee their advantage (in the sense of expanding the set of individual rational deals) over the more classic proposal-based approaches to negotiation.

Adaptive conceding strategies for automated trading agents in dynamic, open markets

One of the crucial issues of automated negotiation in multi-agent systems is how to reach an agreement when a negotiation environment becomes open and dynamic. Even though some strategies have been proposed by researchers, most of them can only work within a static negotiation environment. In this paper, we present a model for designing a strategy for agents that makes adjustable rates of concession by negotiating according to the changes of environments with uncertain and dynamic outside options. This proposal is based on the market-driven agents (MDAs) model, and is guided by four factors in order to determine the degree of concession. These factors are trading opportunity, trading competition, trading time and strategy, and eagerness. The contribution of this paper is extending the MDAs model to an open and dynamic negotiation environment by considering both the current and potential changes of the environment.

A heuristic personality-based bilateral multi-issue bargaining model in electronic commerce

Our everyday lives and specially our commercial transactions involve complex negotiations that incorporate decision-making in a multi-issue setting under utility constraints. Negotiation as a key stage in all commercial transactions has been proliferated by applying decision support facilities that AI techniques provide. Recently, Distributed Artificial Intelligence techniques have been evolved towards multi-agent systems (MASs) where each agent is an intelligent system that solves a specific problem. Incorporating MAS into e-commerce negotiation and bargaining has brought even more potential improvement in efficiency and effectiveness of business systems by automating several of the most time consuming and repetitive stages of the buying process. In bargaining, participants with opposing interests communicate and try to find mutually beneficial agreements by exchanging compromising proposals. However, recent studies on commercial bargaining and negotiation in MASs lack a personality model. Indeed, adding personality to intelligent agents makes them more human-like and increases their flexibility. We investigate the role of personality behaviors of participants in multi-criteria bilateral bargaining in a single-good e-marketplace, where both parties are OCEAN agents based on the five-factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Negative emotions) model of personality. We do not aim to determine strategies that humans should use in negotiation, but to present a more human-like model to enhance the realism of rational bargaining behavior in MASs. First, this study presents a computational approach based on a heuristic bargaining protocol and a personality model, and second, considers the issue of what personality traits and behaviors should be investigated in relation to automated negotiations. We show the results obtained via the simulation on artificial stereotypes. The results suggest and model compound personality style behaviors appropriate to gain the best overall utility in the role of buyer and seller agents and with regard to social welfare and market activeness. This personality-based approach can be used as a predictive or descriptive model of human behavior to adopt in appropriate situations in many areas involving negotiation and bargaining (e.g., commerce, business, politics, military, etc.) for conflict prevention and resolution. This model can be applied as a testbed for comparing personality models against each other based on human data in different negotiation domains.

Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions

In developing open, heterogeneous and distributed multi-agent systems researchers often face a problem of facilitating negotiation and bargaining amongst agents. It is increasingly common to use auction mechanisms for negotiation in multi-agent systems. The choice of auction mechanism and the bidding strategy of an agent are of central importance to the success of the agent model. Our aim is to determine the best agent learning algorithm for bidding in a variety of single seller auction structures in both static environments where a known optimal strategy exists and in complex environments where the optimal strategy may be constantly changing. In this paper we present a model of single seller auctions and describe three adaptive agent algorithms to learn strategies through repeated competition. We experiment in a range of auction environments of increasing complexity to determine how well each agent performs, in relation to an optimal strategy in cases where one can be deduced, or in relation to each other in other cases. We find that, with a uniform value distribution, a purely reactive agent based on Cliff's ZIP algorithm for continuous double auctions (CDA) performs well, although is outperformed in some cases by a memory based agent based on the Gjerstad Dickhaut agent for CDA.

Sequential negotiation in multiagent systems for oil spill response decision-making

Sequential negotiation in multiagent systems for oil spill response decision-making

Metropolitan pollution reduction by intelligent negotiation

This paper discusses the issue of pollution reduction in metropolises by means of intelligent negotiation in multi-agent systems. For situations of complete information, it gives a stochastic hill-climbing search algorithm for computing the pollution-reduction solutions; For situations of incomplete information, it puts forward a genetic algorithm for computing the best solutions for every plants subjectively and proposes market-mechanism-based algorithm for computing the emission-redistribution solutions objectively.

BDI 에이전트 환경에서 협상을 위한 에이전트 통신 언어

인간의 협동적인 활동에서 협상은 협동적인 행위를 방해할 수 있는 충돌을 해결하는데 중요한 역할을 담당한다. 이를 위해 멀티 에이전트 시스템에서의 협상이 공통의 에이전트 통신 언어를 사용하는 메시지의 교환을 통해 진행한다고 가정한다. 본 논문에서는 자율적이고, 자기 목표에 충실하면, 한정된 자원을 가진 BDI 에이전트를 위한 합리적인 협상 메타 언어를 가정한다. 또한 에이전트의 정신적인 상태에 바탕을 두고 통신하는 BDI 에이전트를 위한 협상 프로토콜을 제안한다. Negotiation plays a fundamental role in human cooperative activities, allowing people to resolve conflicts that could interfere with cooperative behaviors. Negotiation In multi-agent systems is achieved through the exchange of messages In a shared agent communication language (ACL). We introduce a rational negotiation meta-language for autonomous, self-interested, and resource-bounded artificial BDI agents. We then propose a negotiation protocol for BDI agents with communicative acts based on their mental states.

Negotiation in multi-agent systems

In systems composed of multiple autonomous agents, negotiation is a key form of interaction that enables groups of agents to arrive at a mutual agreement regarding some belief, goal or plan, for example. Particularly because the agents are autonomous and cannot be assumed to be benevolent, agents must influence others to convince them to act in certain ways, and negotiation is thus critical for managing such inter-agent dependencies. The process of negotiation may be of many different forms, such as auctions, protocols in the style of the contract net, and argumentation, but it is unclear just how sophisticated the agents or the protocols for interaction must be for successful negotiation in different contexts. All these issues were raised in the panel session on negotiation.

Agents that reason and negotiate by arguing

The need for negotiation in multi-agent systems stems from the requirement for agents to solve the problems posed by their interdependence upon one another. Negotiation provides a solution to these problems by giving the agents the means to resolve their conflicting objectives, correct inconsistencies in their knowledge of other agents' world views, and coordinate a joint approach to domain tasks which benefits all the agents concerned. We propose a framework, based upon a system of argumentation, which permits agents to negotiate in order to establish acceptable ways of solving problems. The framework provides a formal model of argumentation-based reasoning and negotiation, details a design philosophy which ensures a clear link between the formal model and its practical instantiation, and describes a case study of this relationship for a particular class of architectures (namely those for belief-desire-intention agents).

Conflict Resolution Using Negotiation in Multi-Agent Systems

Conflict Resolution Using Negotiation in Multi-Agent Systems