Automated Negotiation Model Research Articles

ANEGMA: an automated negotiation model for e-markets

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.

Intelligent Application Implementation Model for Automated Agent Negotiation

In the last few years, multi-agents communication has seen an increased concern. The first main driver was an intelligent agent is claimed to be the next generation model for complex and distributed system engineering. Second the agents can automate process and communication on behalf of the agent’s users for proper actions and decisions. There are many concerns about how to implement communications among different agents in term of applied technology. In this paper we propose a REST- API and JSON technology as implementation tools that can be applied to implement intelligent web-based applications. REST is stands for Representational State Transfer which is allowing communication between web-based agents. JSON (JavaScript Object Notation) is a lightweight data format makes it easy for agents to parse and interchange data. This study has merged REST-AP and JSON technology into an automated negotiation model, and then a prototype is developed to successfully apply the proposed model. This model can facilitate application in business application negotiations and even internet of things (IOT) where HTTP protocol is used.

A Co-Opetitive Automated Negotiation Model for Vertical Allied Enterprises Teams and Stakeholders.

Upstream and downstream of supply chain enterprises often form a tactic vertical alliance to enhance their operational efficiency and maintain their competitive edges in the market. Hence, it is critical for an alliance to collaborate over their internal resources and resolve the profit conflicts among members, so that the functionality required by stakeholders can be fulfilled. As an effective solution, automated negotiation for the vertical allied enterprises team and stakeholder will sufficiently make use of emerging team advantages and significantly reduce the profit conflicts in teams with grouping decisions rather than unilateral decisions by some leader. In this paper, an automated negotiation model is designed to describe both the collaborative game process among the team members and the competitive negotiation process between the allied team and the stakeholder. Considering the co-competitiveness of the vertical allied team, the designed model helps the team members making decision for their own sake, and the team counter-offers for the ongoing negotiation are generated with non-cooperative game process, where the profit derived from negotiation result is distributed with Shapley value method according to contribution or importance contributed by each team member. Finally, a case study is given to testify the effectiveness of the designed model.

Erratum to: A supplier offer modification approach based on fuzzy systems for automated negotiation in e-commerce

In E-commerce, numbers of transactions are increasing day by day in B2B and B2C trade. Online negotiation is possible because of automated negotiation. Autonomous entities such as agents could help in these situations. Providing an offer which has maximum utilities for both trading parties into possible shortest time is the main aim of this work. Proposed model applies issue trade-offs strategy in which multiple issues are traded-offs against one another to maximize participant satisfaction. To make trade-offs the model applies a fuzzy system approach. The automated negotiation model in this article has a process without offer generating and exchanging between buyer and supplier agents to explore time-consuming negotiation process in earlier researches. Mediator component searches an optimal offer that satisfies buyer and supplier requirements. The system will utilize fuzzy inference systems to automate negotiation process and considers two effective factors in the negotiation process: requirements and preferences. Requirements are qualitative or quantitative values which the participants assign to issues for negotiation. Preferences of the participants are priorities assigned to the issues. These values express an importance measure of the issues from a participant perspective. Analytic hierarchy process (AHP) is used to get preferences of the issues. Proposed model applies different Fuzzy Inference System (FIS) schemes for qualitative and quantitative negotiation issues to enhance the satisfaction level of buyers and suppliers. Experimental results show that how the model fulfills the main aim of our work.

A warranty based bilateral multi-issue negotiation approach

Purpose – The purpose of this paper is to propose a warranty-based bilateral automated multi-issue negotiation approach. Design/methodology/approach – A methodology for bilateral automated negotiation process is developed considering the targets such as warranty attractiveness, warranty cost, mean time between failures, spare parts cost to the end user over the useful life of the life. The negotiation methodology is explained using different cases of negotiation. The optimization for each negotiation step is carried out using genetic algorithm with elitism strategy. Findings – The result after optimization indicates that the desired target values are achieved and manufacturer obtained desired profit margin. Practical implications – Application of automated negotiation model is illustrated using a real life case of an automobile engine manufacturer. The proposed approach helps the manufacturer of any product to develop a methodology for carrying out the negotiation process. The approach also results into taking warranty-related decisions at the design stage. Originality/value – This paper contributes in proposing a generalized methodology for warranty-based negotiation in which the negotiation is carried out between the manufacturer and the customer.

An E-Negotiation Agent Using Rule Based and Case Based Approaches: A Comparative Study with Bilateral E-Negotiation with Prediction

The research in the area of automated negotiation systems is going on in many universities. This research is mainly focused on making a practically feasible, faster and reliable E-negotiation system. The ongoing work in this area is happening in the laboratories of the universities mainly for training and research purpose. There are number of negotiation systems such as Henry, Kasbaah, Bazaar, Auction Bot, Inspire, and Magnet. Our research is based on making an agent software for E-negotiation which will give faster results and also is secure and flexible. The negotiation partners and contents between the service providers change frequently. The negotiation process can be transformed into rules and cases. Using these features, a new automated negotiation model for agent integrating rule based and case based reasoning can be derived. We propose an E-negotiation system, in which all product information and multiple agent details are stored on the cloud. An E-negotiation agent acts as a negotiator. Agent has user’s details and their requirements for a particular product. It will check rules based data whether any rule is matching with the user requirement. An agent will see case based data to check any similar negotiation case matching to the user requirement. If a case matches with user requirement, then agent will start the negotiation process using case based data. If any rule related requirement is found in the rule base data, then agent will start the negotiation process using rule based data. If both rules based data and cases based data are not matching with the user requirement, then agent will start the negotiation process using Bilateral Negotiation model. After completing negotiation process, agent gives feedback to the user about whether negotiation is successful or not. The product details, rule based data, and case based data will be stored on the cloud. So that system automatically becomes flexible. We also compare E-negotiation agent automated negotiation and behavior prediction system to prove that using rule based and case based approaches system should become fast.

Organizational and Agent-based Automated Negotiation Model for Carpooling

In the carpooling, individuals need to communicate, negotiate and in most cases adapt their daily schedule to enable cooperation. Through negotiation, agents (individuals) can reach complex agreements in an iterative way which meet the criteria for successful negotiation. The result of the negotiation depends on “negotiation mechanism” used to match and on the behavior of the agents involved in the negotiation process. This paper presents an organizational and agent-based model for commuting by candidate carpoolers using a simple negotiation mechanism aimed at finding an acceptable agreement between agents to carpool. Initially, the agents involved in exploration process, search for their partners via some kind of Agent Communication Language (ACL); after finding potential partners, they start a negotiation to find matched partner to carpool. After having found a good match, the agents can carpool for a specified time period. The agents join the carpool group when the negotiation is successful and leave the carpool group when the agreed time period is expired. Agents can be part of several carpool groups sequentially. The first implementation used home and work locations as well as preferred trip start times and carpool periods determined by uniformly sampling given sets. Furthermore a simplistic negotiation mechanism used roughly to produce possible results for the synthetic data. An automated negotiation model is implemented and validated through simulation. The Janus multi-agent platform is used. Future research will mainly focus on the development of behaviorally sound negotiation mechanism.

ADDRESSING UTILITY SPACE COMPLEXITY IN NEGOTIATIONS INVOLVING HIGHLY UNCORRELATED, CONSTRAINT‐BASED UTILITY SPACES

There is an increasing interest in complex automated negotiations, where agents negotiate about multiple, interdependent issues and agent utility functions exhibit low autocorrelation. In these scenarios, the negotiation mechanisms used to find agreement solutions among agents tend to fail due to the complexity of agents’ preference spaces, and this tendency increases as the degree of autocorrelation decreases. In this paper, we propose an automated negotiation model specially tailored for highly uncorrelated utility spaces based on weighted constraints. The model relies on a mediated, auction‐based interaction protocol and a set of heuristic mechanisms for bidding and deal identification. To address the challenges raised by highly uncorrelated utility spaces, we propose to use a quality factor, which allows agents to balance utility and deal probability when placing their bids or when searching for agreement regions among these bids. Experiments show that the proposed negotiation model achieves high optimality results and low failure rates even in negotiation scenarios involving highly uncorrelated utility spaces, thus outperforming previous approaches.

CSET automated negotiation model for optimal supply chain formation

In an effort to compose an optimal supply chain (SC), this paper tries to bring forward a new collaborative agent-based single machine earliness/tardiness (SET) model. It includes the sub-agents, which are designed for fairly coordinating and distributing job requests at the mid-stream levels. Extending from the precedent SET model, collaborative-SET (CSET) has a coordinating collaborative agent, which is responsible for optimising the information flow and scheduling of the whole SC. This is done by coordinating the information flow at the sub-agent between each two streams. In a long run, this new model makes a complex dynamic SC more efficient and shortens response time. A stimulator that implements the algorithms is programmed in order to calculate the amount of information transfer, time and cost incurred between SET and CSET model. The results generally indicate that the more streams a SC has, the better the performance gain is yielded.

MoRVAM: A reverse Vickrey auction system for mobile commerce

Internet auctions bring buyers and sellers together for the purpose of trading goods and services online. In order to get the goods, a buyer must search for items through several auction sites. When the auction starts, the buyer needs to connect to these auction sites frequently so that he/she can monitor the bid states and re-bid. In this paper, we propose an automated negotiation model between two participants, for mobile commerce, using collaborative mobile agents called MoRVAM, which mediates between the buyer and the sellers, and executes bidding asynchronously and autonomously. A new RVT protocol is also implemented to achieve unconditional bid privacy. Advantages of the RVT protocol are addressed as well. All the bidding process can be implemented without revealing losing bid and unnecessary information.

A new reverse auction agent system for m-commerce using mobile agents

In order to get the goods, a buyer must search for the items through several auction sites in Internet. When the auction starts, the buyer needs to connect to these auction sites frequently so that he can monitor the bid states and re-bid. In this paper, we propose an automated negotiation model between two participants, for m-commerce, using collaborative mobile agents called mobile reverse auction agent system (MoRAAS), which mediates between the buyer and the sellers and executes bidding asynchronously and autonomously. This reduces the network load and offers more intelligent bidding. A new double encryption key chain technique and a new RVAP protocol are proposed to achieve unconditional bid privacy. Every losing bidder can control the privacy of their own bids while no trust is needed. Computational cost of our RVAP protocol is reduced by avoiding the costly verifiable encryption technique.