Contract Negotiation in E-Marketplaces

2009 ◽  
pp. 267-275
Author(s):  
Larbi Esmahi ◽  
Elarbi Badidi
Keyword(s):  
Customer Service ◽  
Intelligent Agents ◽  
Virtual Agents ◽  
Electronic Market ◽  
Market Place ◽  
Negotiation Strategy ◽  
Intelligent Computing ◽  
Contract Negotiation ◽  
Auction Models ◽  
Agent Interaction

The advancement in distributed and intelligent computing has facilitated the use of software agents for implementing e-services; most electronic market places offer their customers virtual agents that can do their bidding (i.e., eBay, onSale). E-transactions via shopping agents constitute a promising opportunity in the e-markets (Chen, Vahidov, & Kersten, 2004). It becomes relevant what kind of information and what kinds of bargain policies are used both by agents and by the market place. There are several steps for building e-business: (1) attracting the customer, (2) knowing how they buy, (3) making transactions, (4) perfecting orders, (5) giving effective customer service, (6) offering customers recourse for problems such as breakage or returns, and (7) providing a rapid conclusion such as electronic payment. In the distributed e-market paradigm, these functions are abstracted via agents representing both contractual parts. In recent years, many researchers in intelligent agents’ domain have focused on the design of market architectures for electronic commerce (Fikes, Engelmore, Farquhar, & Pratt, 1995; Schoop & Quix, 2001; Zwass, 1999), and on protocols governing the interaction of rational agents engaged in such transactions (Hogg & Jennings, 1997; Kersten & Lai, 2005). While providing support for direct agent interaction, existing architectures for multiagent virtual markets usually lack explicit facilities for handling negotiation protocols, since they do not provide such protocols as an integrated part of the framework. In this article we will discuss the problem of contract negotiation in e-marketplaces. In the next section, we will present related models commonly used to implement negotiation in e-markets, game theory models, auction models, and contract-net protocols. Then the following section continues with the presentation of a negotiation protocol based on dependency relations. We then present a negotiation strategy based on risk evaluation. The conclusion summarizes the article and paves the further way concerning the truth in the negotiation strategy and the use of temporal aspects on commitments and executions of contracts.

Simulating socially intelligent agents in semantic virtual environments

2008 ◽  
Vol 23 (4) ◽  
pp. 369-388 ◽  
Author(s):  
Francisco Grimaldo ◽  
Miguel Lozano ◽  
Fernando Barber ◽  
Guillermo Vigueras
Keyword(s):  
Virtual Environments ◽  
Social Relations ◽  
Behavioral Problems ◽  
Intelligent Agents ◽  
Three Dimensional ◽  
The Other ◽  
Social Model ◽  
Virtual Agents ◽  
Artificial Society ◽  
Agent Interaction

AbstractThe simulation of synthetic humans inhabiting virtual environments is a current research topic with a great number of behavioral problems to be tackled. Semantical virtual environments (SVEs) have recently been proposed not only to ease world modeling but also to enhance the agent–object and agent–agent interaction. Thus, we propose the use of ontologies to define the world’s knowledge base and to introduce semantic levels of detail that help the sensorization of complex scenes—containing lots of interactive objects. The object taxonomy also helps to create general and reusable operativity for autonomous characters—for example, liquids can be poured from containers such as bottles. On the other hand, we use the ontology to define social relations among agents within an artificial society. These relations must be taken into account in order to display socially acceptable decisions. Therefore, we have implemented a market-based social model that reaches coordination and sociability by means of task exchanges. This paper presents a multi-agent framework oriented to simulate socially intelligent characters in SVEs. The framework has been successfully tested in three-dimensional (3D) dynamic scenarios while simulating a virtual university bar, where groups of waiters and customers interact with both the objects in the scene and the other virtual agents, finally displaying complex social behaviors.

scholarly journals Intelligent Blended Agents: Reality–Virtuality Interaction with Artificially Intelligent Embodied Virtual Humans

2020 ◽  
Vol 4 (4) ◽  
pp. 85
Author(s):  
Susanne Schmidt ◽  
Oscar Ariza ◽  
Frank Steinicke
Keyword(s):  
Intelligent Agents ◽  
User Study ◽  
Physical Simulation ◽  
Body Representation ◽  
Human Interaction ◽  
Communication Process ◽  
Virtual Agents ◽  
Positive Effects ◽  
Human Agent ◽  
Agent Interaction

Intelligent virtual agents (VAs) already support us in a variety of everyday tasks such as setting up appointments, monitoring our fitness, and organizing messages. Adding a humanoid body representation to these mostly voice-based VAs has enormous potential to enrich the human–agent communication process but, at the same time, raises expectations regarding the agent’s social, spatial, and intelligent behavior. Embodied VAs may be perceived as less human-like if they, for example, do not return eye contact, or do not show a plausible collision behavior with the physical surroundings. In this article, we introduce a new model that extends human-to-human interaction to interaction with intelligent agents and covers different multi-modal and multi-sensory channels that are required to create believable embodied VAs. Theoretical considerations of the different aspects of human–agent interaction are complemented by implementation guidelines to support the practical development of such agents. In this context, we particularly emphasize one aspect that is distinctive of embodied agents, i.e., interaction with the physical world. Since previous studies indicated negative effects of implausible physical behavior of VAs, we were interested in the initial responses of users when interacting with a VA with virtual–physical capabilities for the first time. We conducted a pilot study to collect subjective feedback regarding two forms of virtual–physical interactions. Both were designed and implemented in preparation of the user study, and represent two different approaches to virtual–physical manipulations: (i) displacement of a robotic object, and (ii) writing on a physical sheet of paper with thermochromic ink. The qualitative results of the study indicate positive effects of agents with virtual–physical capabilities in terms of their perceived realism as well as evoked emotional responses of the users. We conclude with an outlook on possible future developments of different aspects of human–agent interaction in general and the physical simulation in particular.

Biosolids management integrated into national organic resource strategies in the UK

2007 ◽  
Vol 2 (4) ◽  
Author(s):  
P.J. Matthews
Keyword(s):  
Customer Service ◽  
Food Processing ◽  
Special Treatment ◽  
Market Place ◽  
Playing Field ◽  
Municipal Compost ◽  
Starting Point ◽  
Organic Resource ◽  
The Uk ◽  
Organic Resources

There are many kinds of organic byproducts. They are potentially useful, but can be wasted and thrown away. One use for many of these products is as fertilisers and soil conditioners but they are managed and regulated separately. Customers are faced with choices of services and products. Examples are biosolids, municipal composts, food processing byproducts and farm yard manures. Biosolids are perceived as being special, but part of a range of a number of wastes seeking a disposal. The target must be to establish and maintain safe, sustainable and welcome operations for the supply of all of these products. Trust is at the heart. There is nothing special about biosolids; they should not demand special treatment and should be viewed as one of a range of safe products. There must be a ‘level playing field’ for all products and then customers can choose that which is most suitable for their needs on the basis of agronomic value, customer service and financial deals available. So, for example, municipal compost and biosolids should compete in the market place on the basis of normal commercial terms, but not on the basis of differential safety or quality. It behoves everyone to co-operate in creating the starting point of equality of opportunity. The UK has established the Sustainable Organic Resources Partnership to bring together all stakeholders for all kinds of organic resources. The objective has been to create a national focus of knowledge excellence, which can provide the confidence for building public trust. The paper describes the history, role and future of SORP.

scholarly journals Perceptions on Authenticity in Chat Bots

2018 ◽  
Vol 2 (3) ◽  
pp. 60 ◽  
Author(s):  
Mario Neururer ◽  
Stephan Schlögl ◽  
Luisa Brinkschulte ◽  
Aleksander Groth
Keyword(s):  
Intelligent Agents ◽  
Social Intelligence ◽  
Virtual Agents ◽  
Learning From Experience ◽  
Alan Turing ◽  
Conversational Behavior ◽  
Artificial Intelligence Technology ◽  
Intelligent Virtual Agents ◽  
Open Question ◽  
Question Survey

In 1950, Alan Turing proposed his concept of universal machines, emphasizing their abilities to learn, think, and behave in a human-like manner. Today, the existence of intelligent agents imitating human characteristics is more relevant than ever. They have expanded to numerous aspects of daily life. Yet, while they are often seen as work simplifiers, their interactions usually lack social competence. In particular, they miss what one may call authenticity. In the study presented in this paper, we explore how characteristics of social intelligence may enhance future agent implementations. Interviews and an open question survey with experts from different fields have led to a shared understanding of what it would take to make intelligent virtual agents, in particular messaging agents (i.e., chat bots), more authentic. Results suggest that showcasing a transparent purpose, learning from experience, anthropomorphizing, human-like conversational behavior, and coherence, are guiding characteristics for agent authenticity and should consequently allow for and support a better coexistence of artificial intelligence technology with its respective users.

A European Electronic Market Place for Tourism

1995 ◽  
pp. 22-32
Author(s):  
P. F. Byerley ◽  
J. Ewers ◽  
Gregory Lella ◽  
Gianluca Lo Reto ◽  
Uwe Zobel
Keyword(s):  
Electronic Market ◽  
Market Place

Shortening Automated Negotiation Threads via Neural Nets

2011 ◽  
pp. 1418-1425
Author(s):  
Ioanna Roussaki ◽  
Ioannis Papaioannou ◽  
Miltiades Anagnostou
Keyword(s):  
Intelligent Agents ◽  
Autonomous Agents ◽  
Research Field ◽  
Future Research ◽  
Negotiation Strategy ◽  
Negotiation Strategies ◽  
Negotiation Protocol ◽  
Automated Negotiations ◽  
Formal Problem ◽  
To Come

In the artificial intelligence domain, an emerging research field that rapidly gains momentum is Automated Negotiations (Fatima, Wooldridge, & Jennings, 2007) (Buttner, 2006). In this framework, building intelligent agents (Silva, Romão, Deugo, & da Silva, 2001) adequate for participating in negotiations and acting autonomously on behalf of their owners is a very challenging research topic (Saha, 2006) (Jennings, Faratin, Lomuscio, Parsons, Sierra, & Wooldridge, 2001). In automated negotiations, three main items need to be specified (Faratin, Sierra, & Jennings, 1998) (Rosenschein, & Zlotkin, 1994): (i) the negotiation protocol & model, (ii) the negotiation issues, and (iii) the negotiation strategies that the agents will employ. According to (Walton, & Krabbe, 1995), “Negotiation is a form of interaction in which a group of agents, with conflicting interests and a desire to cooperate try to come to a mutually acceptable agreement on the division of scarce resources”. These resources do not only refer to money, but also include other parameters, over which the agents’ owners are willing to negotiate, such as product quality features, delivery conditions, guarantee, etc. (Maes, Guttman, & Moukas, 1999) (Sierra, 2004). In this framework, agents operate following predefined rules and procedures specified by the employed negotiation protocol (Rosenschein, & Zlotkin, 1994), aiming to address the requirements of their human or corporate owners as much as possible. Furthermore, the negotiating agents use a reasoning model based on which their responses to their opponent’s offers are formulated (Muller, 1996). This policy is widely known as the negotiation strategy of the agent (Li, Su, & Lam, 2006). This paper elaborates on the design of negotiation strategies for autonomous agents. The proposed strategies are applicable in cases where the agents have strict deadlines and they negotiate with a single party over the value of a single parameter (single-issue bilateral negotiations). Learning techniques based on MLP and GR Neural Networks (NNs) are employed by the client agents, in order to predict their opponents’ behaviour and achieve a timely detection of unsuccessful negotiations. The proposed NN-assisted strategies have been evaluated and turn out to be highly effective with regards to the duration reduction of the negotiation threads that cannot lead to agreements. The rest of the paper is structured as follows. In the second section, the basic principles of the designed negotiation framework are presented, while the formal problem statement is provided. The third section elaborates on the NN-assisted strategies designed and provides the configuration details of the NNs employed. The fourth section presents the experiments conducted, while the fifth section summarizes and evaluates the results of these experiments. Finally, in the last section, conclusions are drawn and future research plans are exposed.

On distributed artificial intelligence

1988 ◽  
Vol 3 (1) ◽  
pp. 21-57 ◽  
Author(s):  
Luis Eduardo ◽  
Castillo Hern
Keyword(s):  
Artificial Intelligence ◽  
Conceptual Model ◽  
Intelligent Agents ◽  
Distributed Artificial Intelligence ◽  
Agent Interaction ◽  
Multi Agent

AbstractDistributed Artificial Intelligence has been loosely defined in terms of computation by distributed, intelligent agents. Although a variety of projects employing widely ranging methodologies have been reported, work in the field has matured enough to reveal some consensus about its main characteristics and principles. A number of prominent projects are described in detail, and two general frameworks, theSystem conceptual modeland theagent conceptual model, are used to compare the different approaches. The paper concludes by reviewing approaches to formalizing some of the more critical capabilities required by multi-agent interaction.

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