Negotiation Agents' Decision Making Using Markov Chains

An, Bo; Sim, Kwang Mong; Tang, Liang; Miao, Chunyan; Shen, Zhi Qi; Cheng, Dai Jie

doi:10.1007/978-3-540-76282-9_1

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…In this paper, we adopt the "sitand-wait" strategy proposed by [4]. The "sit-and-wait" strategy is used in the case when the negotiation issue does not devaluate over time and has been proven by [4] that this strategy is the dominant strategy for an agent using time-dependent negotiation process, regardless of the strategy that its trading partner adopts. This strategy is very practical for two reasons:…”

Section: Negotiation Strategymentioning

confidence: 98%

“…In particular, the strategy of making concessions and evaluating the incoming offers and of making decisions to interact with the negotiation opponent. This paper adopts the work of [4] in defining a concession strategy. Each agent is assumed to have a different time preference, i.e., its time deadline.…”

Section: Negotiation Strategymentioning

confidence: 99%

“…• Since a bargaining negotiation is fundamentally time-dependent [4], we assume that both the agent representing consumers and the service provider agent utilize a time-dependent strategy while making a concession. This assumption is an appealing one since human traders easily understand deadlines, and it is trivially simple to specify a deadline to a software agent.…”

Section: Negotiation Agentmentioning

confidence: 99%

“…In general, the proposal of agent A to its trading partner at round t (0 ≤ t ≤ T A deadline ) is modeled as a time-dependent function as follows [4]:…”

Section: Negotiation Strategymentioning

confidence: 99%

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Knowledge-empowered agent information system for privacy payoff in eCommerce

et al. 2011

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Today, many online companies are gathering information and assembling sophisticated databases that know a great deal of information about many people, generally without the knowledge of those people. Such endeavor has resulted in the unprecedented attrition of individual's right to informational self-determination. On the one hand, Consumers are powerless to prevent the unauthorized dissemination of their personal information, and on the other, they are excluded from its profitable commercial exchange. This paper focuses on developing knowledge-empowered agent information system for privacy payoff as a means of rewarding consumers for sharing their personal information with online businesses. The design of this system is driven by the following argument: if consumers' personal information is a valuable asset, should they not be entitled to benefit from their asset as well? The proposed information system is a multi-agent system where several agents employ various knowledge and requirements for personal information valuation and interaction capabilities that most users cannot do on their own. The agents in the information system bear the responsibility of working on behalf of consumers to categorize their personal data objects, report to consumers on online businesses' trust and reputation, determine the value of their compensation using risk-based financial models, and finally negotiate for a payoff value in return for the dissemination of users' information. The details of the system as well as a proof-of-concept implementation using JADE (Java Agent Development Environment) are presented here.

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Section: Negotiation Strategymentioning

confidence: 98%

Section: Negotiation Strategymentioning

confidence: 99%

Section: Negotiation Agentmentioning

confidence: 99%

“…In general, the proposal of agent A to its trading partner at round t (0 ≤ t ≤ T A deadline ) is modeled as a time-dependent function as follows [4]:…”

Section: Negotiation Strategymentioning

confidence: 99%

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