Scalable Complex Contract Negotiation with Structured Search and Agenda Management
Authors: Xiaoqin Zhang, Mark Klein, Ivan Marsa-Maestre
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate the contributions of our approach, 1) we developed our proposed negotiation model using a hierarchical problem structure and a constraint-based preference model for real-world applications; 2) we defined a scenario matrix to capture various characteristics of negotiation scenarios and developed a scenario generator that produces test cases according to this matrix; and 3) we performed an extensive set of experiments to study the performance of this structured negotiation protocol and the influence of different scenario parameters, and investigated the Pareto efficiency and social welfare optimality of the negotiation outcomes. |
| Researcher Affiliation | Academia | Xiaoqin Shelley Zhang Dept. of Computer & Information Science University of Massachusetts-Dartmouth Dartmouth, MA, U.S.A. shelley.zhang@umassd.edu Mark Klein Center for Collective Intelligence MIT Sloan School of Management Cambridge, MA, U.S.A. m klein@mit.edu Ivan Marsa-Maestre Computer Engineering Department University of Alcala, Spain ivan.marsa@uah.es |
| Pseudocode | Yes | all constraints over DGi Γ; bid set Θ ; BL bids limit provided by the mediator; while = |Θ| < BL do new Bids = find Bids( ); for all B new Bids do preference(B) = P γ Sat.(B,γ) weight(γ); end for Θ new Bids Θ; γmin arg minγ weight(γ) remove γmin from ; end while |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public availability of its source code. |
| Open Datasets | No | The paper describes how scenarios were generated for experiments ('developed a scenario generator that produces test cases according to this matrix') but does not provide access information (link, DOI, or formal citation) for a publicly available dataset used or generated. |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., percentages or counts for training, validation, or test sets). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | To better understand the influence of the input problem structure on the performance of this protocol, we defined five parameters to capture the topological and the interdependent characteristic of the problem structure... We generated scenarios with 50 and 100 issues. ... A preferred group size limit parameter can be used to influence group formation. ... In the current implementation, L is set as 2|DG|, bounded by two constant parameters [min Bids Num, max Bids Num]. ... max Bids Limit (a constant parameter, value 20 is used currently for hierarchical negotiation). |