Lifecycle Model of a Negotiation Agent: A Survey of Automated Negotiation Techniques
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Lifecycle Model of a Negotiation Agent: A Survey of Automated Negotiation Techniques Usha Kiruthika1 · Thamarai Selvi Somasundaram2 · S. Kanaga Suba Raja3 Accepted: 28 August 2020 © Springer Nature B.V. 2020
Abstract Negotiation is a complex process. The decision making involved in several stages of negotiation makes its automation complex. In this paper we present a lifecycle model of a negotiation agent in which we identify the individual components that comprise automated negotiation and the interactions between those components. We present a survey of methods used in the automated negotiation literature fitting them to the components of our lifecycle model. While discussing the opponent modeling component, we present the taxonomy of opponent models. The lifecycle model is generic enough to accommodate most of the frameworks in the literature. To this end we fit the methods used in some of the automated negotiation frameworks in the literature to the lifecycle. Keywords Automated negotiation · Lifecycle model · Multi-agent systems · Agentbased e-commerce
1 Introduction Automated negotiation has gained importance in the recent years owing to the growth in e-commerce and cloud-based applications. In a multi-agent environment, a negotiating agent exhibits autonomy and hence does not require a human during * Usha Kiruthika [email protected]; [email protected] Thamarai Selvi Somasundaram [email protected] S. Kanaga Suba Raja [email protected] 1
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, India
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Department of Computer Technology, Madras Institute of Technology, Chennai, India
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Department of Information Technology, Easwari Engineering College, Chennai, India
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negotiation. But the initial requirements need to be specified by a human before the actual negotiation begins. The complexity involved in decision-making during automated negotiation has sparked much research in this area. Automated negotiation may make use of artificial intelligence techniques (Gerding et al. 2000; Kraus 1997; Li et al. 2003), game theory (Gerding et al. 2000; Kraus 1997; Li et al. 2003; Jennings et al. 2001; Binmore and Vulkan 1999; Liang and Yuan 2008; Osborne and Rubinstein 1990; Rubinstein 1982; Chen et al. 2002; Chatterjee 1996) or evolutionary programming (Choi et al. 2001; de Jonge and Sierra 2016; Tu et al. 2000). In our survey, we primarily focus on bilateral negotiations which are negotiations between exactly two participants. We refer to the participants as ‘agents’ as every participant is expected to exhibit autonomy in an automated negotiation setting. The agents send offers and receive counter-offers. An offer is a set of values for a set of attributes over which the agents negotiate. Offers or counter offers are generated by the agents based on their own set of ‘preferences’. Preferences denote a preferred set of values of attributes that are being negotiated. Preferences are usually
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