Learning-based automated negotiation between shipper and forwarder

Rau, Hsin; Tsai, Mou-Hsing; Chen, Chao-Wen; Shiang, Wei-Jung

doi:10.1016/j.cie.2006.08.008

Cited by 35 publications

(18 citation statements)

References 20 publications

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“…If it is known that the opponent employs a time-dependent tactic, work by Rau et al and Papaioannou et al [151,153,160] can be used. Owing to the reduced search space of time-dependent tactics, Rau et al [160] find that the concession tactic and weight of every issue offered by the opponent can be learned from this process in an exact manner. Papaioannou compares the performance of five estimators for the opponent's bidding strategy, of which we have already discussed three regression-based estimators in Sect.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…3. Reach win-win agreements [11,12,15,21,30,50,51,81,83,90,94,103,113,123,124,137,143,146,149,155,160,165,166,174,183,184,188,194,195,198,[205][206][207] In a cooperative environment, agen ts aim for a fair result, for example because there might be opportunity for future negotiations. Cooperation, however, does not necessarily imply that the parties share explicit information about their preferences or strategy, as agents may still strive for a result that is beneficial for themselves and acceptable for their opponent.…”

Section: Learning About the Opponentmentioning

confidence: 99%

“…Heuristics [7][8][9]30,[75][76][77]163,194,195] 5.4 Bidding strategy Regression analysis Bayesian networks [139] Genetic algorithms [151] Non-linear regression [1,28,73,85,153,161,204] Polynomial interpolation [153] Time series forecasting Derivatives [29,137] Markov chains [140] Neural networks [35,36,115,133,146,151,153,160] Signal processing [44][45][46]132,150,197,199] …”

Section: Learning the Acceptance Strategymentioning

confidence: 99%

“…Fairness is an important aspect, especially when the parties expect to conduct future negotiations. The Nash solution ω Nash in a bilateral negotiation is defined as the outcome that maximizes the product of the utilities: Average utility [11,12,28,29,44,45,50,51,57,58,72,73,75,[81][82][83]85,92,94,123,124,133,140,143,[149][150][151]155,173,183,184,188,194,197,204,207] Distance to a fair outcome [11,81,82,137] Distance to Pareto frontier [11,113,143,155,160] Joint utility [50,90,103,…”

Section: Performance Measures For Negotiation Strategiesmentioning

confidence: 99%

“…Minimize negotiation cost [7][8][9]11,50,72,73,90,103,113,137,143,146,149,151,153,155,160,165,166,183,184,188,[205][206][207] In general, it costs time and resources to negotiate. As a consequence, (early) agreements are often preferred over not reaching an agreement.…”

Section: Learning About the Opponentmentioning

confidence: 99%

See 4 more Smart Citations

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

123

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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: Artificial Neural Networkmentioning

confidence: 99%