e-Negotiation Systems and Software Agents: Methods, Models, and Applications

Braun, Peter; Brzostowski, Jakub; Kersten, Gregory E.; Kim, Jin Baek; Kowalczyk, Ryszard; Strecker, Stefan; Vahidov, Rustam

doi:10.1007/1-84628-231-4_15

Cited by 45 publications

(35 citation statements)

References 48 publications

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“…A number of surveys have been conducted on the general topic of automated negotiation, for example by Jennings et al [91], Kraus [107], Braun et al [25], and Li et al [118]. Jennings et al argue that automated negotiation is a main concern for multi-agent system research [91]; and Kraus examines economic theory and game-theory techniques for reaching agreements in multi-agent environments [107].…”

Section: Related Surveysmentioning

confidence: 99%

“…Jennings et al argue that automated negotiation is a main concern for multi-agent system research [91]; and Kraus examines economic theory and game-theory techniques for reaching agreements in multi-agent environments [107]. Braun et al review electronic negotiation systems and negotiation agents, concisely describing how learning techniques have been used to learn characteristics of the opponent [25]. Li et al distinguish different types of negotiation, and briefly discuss opponent modeling [118].…”

Section: Related Surveysmentioning

confidence: 99%

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Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

Baarslag

Hendrikx

Hindriks

et al. 2015

Auton Agent Multi-Agent Syst

121

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A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent's preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other's wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy.

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Section: Related Surveysmentioning

confidence: 99%

Section: Related Surveysmentioning

confidence: 99%

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

Baarslag

Hendrikx

Hindriks

et al. 2015

Auton Agent Multi-Agent Syst

121

View full text Add to dashboard Cite

show abstract

“…For the support of electronic negotiations several systems have been created and analyzed over the last two decades (Köszegi et al 2006;Braun et al 2006;. Depending on their focus, protocol(s) supported and technologies deployed, they differ considerably in their functionalities and can be classified accordingly into different categories (Kersten and Lai 2007;Kersten 2004).…”

Section: Related Workmentioning

confidence: 99%

“…Depending on their focus, protocol(s) supported and technologies deployed, they differ considerably in their functionalities and can be classified accordingly into different categories (Kersten and Lai 2007;Kersten 2004). Some of the systems supporting contract negotiation (Yuan and Turel 2004) focus on research and training, document content or process support (Braun et al 2006). In the B2B environment, transactions can become highly complex when multiple items with various attributes are negotiated.…”

Section: Related Workmentioning

confidence: 99%

Context-Sensitive Semantic Synchronization Enablement in Electronic Negotiations

Paulheim¹,

Fengel²,

Rebstock³

2010

Group Decis Negot

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When integrating electronic negotiations into real-world business processes, an important problem arising is the magnitude of e-business standards in use. In order to support electronic negotiations when negotiation partners use and need to integrate terms named and formatted according to different document standards, taxonomies or similar standards, references between those standards are needed. In this paper, we present a framework for a negotiation support meta-tool which is designed to maintain and dynamically evolve a collection of such references. To deal with semantic ambiguities in different contexts, it provides users with context-sensitive references. The framework uses technologies from ontological engineering and machine learning, its services can be integrated into existing negotiation applications via web services. Furthermore, it allows parameterzation for the actual domain of use.

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“…(Braun et al, 2006) define negotiation as "a decentralized decision-making process used to search for and arrive at an agreement that satisfies the requirements of two or more parties in the presence of limited common knowledge and conflicting preferences." The research focus of most of the computational literature, however, is different in that it focuses on providing negotiation support systems (or e-Negotiation systems) to enable automated negotiations between intelligent, autonomous agents, or to design automated negotiation agents to negotiate with human counterparts, or to help and advise negotiators during the various phases during the negotiation process.…”

Section: Introductionmentioning

confidence: 99%

Toward a Unified Negotiation Framework: Leveraging Strengths in Behavioral and Computational Communities

Turan

Dai

Sycara

et al. 2013

Models for Intercultural Collaboration and Negotiation

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Abstract. While there has been a large body of negotiation literature in both Behavioral Science (behavioral) and Artificial Intelligence/Game Theory (computational) communities, there has not been an attempt to bridge the two communities to our best knowledge. In this paper, we compare and contrast the characteristics of behavioral and computational literature in negotiation. We discover that incorporating the strengths of two types of literature are valuable in expanding the horizon of research outlook.

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e-Negotiation Systems and Software Agents: Methods, Models, and Applications

Cited by 45 publications

References 48 publications

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

Context-Sensitive Semantic Synchronization Enablement in Electronic Negotiations

Toward a Unified Negotiation Framework: Leveraging Strengths in Behavioral and Computational Communities

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