Modeling constraint-based negotiating agents

Wang, Huaiqing; Liao, Stephen Shaoyi; Liao, Lejian

doi:10.1016/s0167-9236(01)00138-5

Cited by 37 publications

(12 citation statements)

References 23 publications

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“…Backtracking has been extensively studied and has long been used as a strategy for solving combinatorial problems [20,21]. The major advantage of the backtracking algorithm is that it is systematic and can guarantee to find a consistent solution given enough time [22]. In this study, the backtracking algorithm is applied for determining the location of DG units when a fault occurs in a distribution system with DG units.…”

Section: Backtracking Algorithmmentioning

confidence: 99%

A novel neural network and backtracking based protection coordination scheme for distribution system with distributed generation

Zayandehroodi

Mohamed

Shareef

et al. 2012

International Journal of Electrical Power & Energy Systems

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Section: Backtracking Algorithmmentioning

confidence: 99%

A novel neural network and backtracking based protection coordination scheme for distribution system with distributed generation

Zayandehroodi

Mohamed

Shareef

et al. 2012

International Journal of Electrical Power & Energy Systems

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“…As a summary, there are variety of approaches of negotiation knowledge discovery such as case-based reasoning [3], fuzzy rules [6], time series approximation [24], Bayesian learning [5,40], Markov Chain Process [25], evolutionary learning [19], constraint satisfaction [38], etc. Generally speaking, these learning approaches can be classified into the broad categories of parametric [3,19,24] or non-parametric methods [5,25,40].…”

Section: Related Workmentioning

confidence: 99%

Knowledge discovery for adaptive negotiation agents in e-marketplaces

Lau

Song

et al. 2008

Decision Support Systems

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Intelligent software agents are promising in improving the effectiveness of e-marketplaces for e-commerce. Although a large amount of research has been conducted to develop negotiation 2 protocols and mechanisms for e-marketplaces, existing negotiation mechanisms are weak in dealing with complex and dynamic negotiation spaces often found in e-commerce. This paper illustrates a novel knowledge discovery method and a probabilistic negotiation decision making mechanism to improve the performance of negotiation agents. Our preliminary experiments show that the probabilistic negotiation agents empowered by knowledge discovery mechanisms are more effective and efficient than the Pareto optimal negotiation agents in simulated e-marketplaces.

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“…This has been reemphasized in the special issue of the DSS journal on the future directions of DSS [3,25]. IAs appear in an increasing number of DSS applications and IAs' properties can facilitate active decision making.…”

Section: Intelligent Agents Assisted Decision Support Systemsmentioning

confidence: 99%