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2007, Proceedings of the 2007 ACM symposium on Applied computing - SAC '07
The automated negotiation topic plays an important role in e-commerce research. However, despite considerable work on automated negotiation, few research efforts have aimed at software engineering facilities such a reuse and flexibility. To address this issue, we propose a novel computation environment for building agents with flexible negotiation strategies to function in various virtual business domains. Regarding the negotiation strategies, some decision taking assistance techniques may be used in group, e.g. rule-based reasoning and techniques to machine learning. Considering the flexibility of acting in various business domains, a specific agent can be programmed to work in as many business domains as necessary, and essentially, it will also be possible to reconfigure the agent´s business domains at execution time. All the experiments in this work were idealized in the terms of a tourism negotiation package. Results from these experiments demonstrate effectiveness of our proposal.
2007
The automated negotiation topic plays an important role in e-commerce research. However, despite considerable work on automated negotiation, few research efforts have aimed at software engineering facilities such a reuse and flexibility. To address this issue, we propose a novel computation environment for building agents with flexible negotiation strategies to function in various virtual business domains. Regarding the negotiation strategies, some decision taking assistance techniques may be used in group, e.g. rule-based reasoning and techniques to machine learning. Considering the flexibility of acting in various business domains, a specific agent can be programmed to work in as many business domains as necessary, and essentially, it will also be possible to reconfigure the agent´s business domains at execution time.
2005
The note reports on the current status of an implementation of a rule-based negotiation mechanism in a model e-commerce multi-agent system. Here, we briefly describe the conceptual architecture of the system and its initial implementation utilizing JADE and JESS. A particular negotiation scenario involving English auctions performed in parallel is also discussed.
Advanced Research on Cloud Computing Design and Applications, 2015
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, Magnet. Our research is based on making an agent software for E-negotiation which will give faster results and also is secure and flexible. Cloud Computing provides security and flexibility to the user data. Using these features we propose an E-negotiation system, in which, all product information and agent details are stored on the cloud. This system proposes three conditions for making successful negotiation. First rule based, where agent will check user requirements with rule based data. Second case based, where an agent will see case based data to check any similar previous negotiation case is matching to the user requirement. Third bilateral negotiation model, if both rules based data and case based data are not matching with the user requirement, then agent use bilateral negotiation model for negotiation. After completing negotiation process, agents give feedback to the user about whether negotiation is successful or not. Using rule based reasoning and case based reasoning this system will improve the efficiency and success rate of the negotiation process.
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.
2001
Electronic Commerce technology has changed the way traditional business is being done. Transactions’ complexity is increased due both to the huge amount of available information and also to the environment dynamics. Moreover, Electronic Commerce has enabled the arising of new economical structures, as it is the case of Virtual Organisations. Our research aims at providing flexible and general-purpose systems for intelligent negotiation, both for Electronic Commerce and Virtual Organisation formation. This paper proposes an Electronic Market architecture implemented through a Multi-Agent system. This architecture includes both a specific market agent which plays the role of market coordinator, as well as agents representing the individual business partners with their own goals and strategies. We also include a sophisticated negotiation protocol through multi-criteria and distributed constraint formalisms. An online, continuous reinforcement learning algorithm has been designed to enable agents to adapt themselves according to the changing environment, including the competitor agents.
2003
Abstract. Support for negotiation is one of the more important research issues when developing agent systems utilized in e-commerce. While, depending on the type of the transaction, different negotiation procedures need to be utilized, only very few proposed frameworks are generic and flexible enough to handle multiple scenarios. This note presents negotiating agents, which can change their negotiation strategy depending on circumstances.
Autonomous Agents and Multi-Agent Systems, 2021
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.
Utilizing Social Media to Engage Consumers, 2013
Electronic negotiation is one of many applications that software agents can perform to facilitate electronic business. Negotiations between software agents and humans (hybrid negotiation), can make electronic business efficient and intelligent. It can save time, effort and other valueable resources by replacing the human in electronic business activities and many other domains. However, to enable hybrid negotiation, a software agent needs clear machine interpretable semantics to understand and generate natural language content. Although it is not simple to make natural language content understandable by software agents as a whole, it can be achieved in different domains-in this case electronic business. For this purpose, an example of hybrid negotiation is presented, in which a software agent and a human agent negotiate for a business contract. Problems involved in this negotiation process are partially resolved through ontologies (the main Semantic Web technology), NSS (negotiation support system) and hand written rules.
web: http://www.tdg-seville.info Palabras clave: software architectures, web engineering, SLAs, negotiation
— E-commerce is seen as one of the key services of modern information society. The idea of automating e-commerce transactions attracted a lot of interest during the last years. Multi-agent systems are claimed to be one of promising software technologies for achieving this goal. Rules constitute a very promising approach to describing negotiation processes. We suggest ECA rule-based approaches to automated negotiations. The experimental scenario considers multiple buyer and seller agents that are performed in parallel.
2003
Abstract. Support for negotiation is one of the more important research issues when developing agent systems utilized in e-commerce. While, depending on the type of the transaction, different negotiation procedures need to be utilized, only very few proposed frameworks are generic and flexible enough to handle multiple scenarios. This paper presents negotiating agents, which can change their negotiation protocol and strategy through dynamic loading of reasoning models.
Journal of Software Engineering and Applications, 2015
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, S. R. Vij et al. 522 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.
International Conference on Enterprise Information Systems, 2003
To support fully automatic business cycles, information systems for electronic commerce need to be able to conduct negotiation automatically. In recent years, a number of general frameworks for automated negotiation have been proposed. Application of such frameworks in a specific negotiation situation entails selecting the proper framework and adapting it to this situation. This selection and adaptation process is driven
Knowledge and Information Systems, 2008
This paper presents a knowledge empowered automated negotiation system for buyer-centric multi-bilateral multi-attribute e-Procurement. We propose two knowledge empowered models namely KERM and KACM. KERM is used for the buyer to determine a list of suppliers which are the best qualified candidates to negotiate with. KERM also allows the flexibility to assign appropriate weights, based on buyer's interests, to each knowledge factor affecting the overall evaluation result of a quote. The resulted list of quotes of high rank is believed to produce satisfactory negotiation result for the buyer. KACM enables an automated concession process, while at the same time facilitates a flexible negotiation via the use of concept switch and tagged rules. KACM emphasizes the utilization of knowledge originated from the historical negotiation data in estimating and fine-tuning the negotiation parameters for improving the performance of automated negotiation. Our results show that the prototype makes significant improvement in the satisfaction level of negotiation results.
International Journal of Modern Trends in Engineering & Research, 2017
Domain oriented negotiation is the emergent functionality of automated E-Commerce. There are several model deployed by various researcher in there automated E-Commerce model for domain oriented negotiation strategies. In this research review paper we provide a review on various negotiation models which are deployed in various domain oriented negotiation.
Scalable Computing: Practice and Experience, 2014
An automated negotiation environment, in which agents employ different bargaining strategies is described. During negotiation, as more information is exchanged in the negotiation rounds, the agents can change the preferences for certain attributes of the negotiation object. The multi-agent system is developed for a real estate agency business model and several use cases scenarios, using intelligent software agents, are implemented.
2003
In the last few years we have witnessed a surge of business-to-consumer and business-to-business commerce operated on the Internet. However, most current electronic commerce systems are little more than electronic catalogues that allow a user to purchase a product under predetermined and inflexible terms and conditions. We believe that in the next few years we will see a new generation of electronic commerce systems emerge, based on automated negotiation. In this paper, we identify the main parameters on which any automated negotiation depends. To show the applicability of our classification framework, we use it to categorise a representative sample of some of the most prominent negotiation models that exist in the literature.
Proceedings of the International Scientific Conference - Sinteza 2020
Artificial intelligence can perform many different tasks in negotiation, reducing time and effort on the part of human negotiators. Today there is an increasing number of negotiation support systems and automated negotiating agents, that can assist human negotiators before and during the process. They can serve as simulators and training tools, but also conduct negotiations more or less autonomously. The aim of this paper is to review different forms of electronic negotiations and current issues in this field.
International Journal of Engineering Research and, 2016
Online or E-Shopping requires a great effort of investigation to find the best deal. Although immense information is available on World Wide Web to make smart decision in shopping, however for analyzing, comparing, and making purchase decision in e-business transaction, humans are required. Most of the developed E-Commerce businesses are still based on human intelligence. To save this investigation cum effort time, we can automate this task using some software agents which will automate this e-commerce shopping. Not just automation, we can also put the negotiation capabilities into the software agent which can help us to get the best available deal. Software agents help to automate the process of buying and selling goods and services in E-Market. To enhance the shopping process agents are the useful tools and acts as a channel between traditional market and automated E-market. This paper presents usage of negotiation protocols and intelligent agent negotiation strategies which have been mentioned in literature, responsible for price negotiation. The conclusion here is to work in automating ecommerce transaction(s) using improved negotiation strategies to enhance the experience of e-business by reducing the human effort.
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