Addressing Complexity in Multi-Issue Negotiation via Utility Hypergraphs
Authors: Rafik Hadfi, Takayuki Ito
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated our model using parametrized random hypergraphs, showing that it can optimally handle complex utility spaces while outperforming previous sampling approaches. 3 Experimental Results |
| Researcher Affiliation | Academia | Rafik Hadfiand Takayuki Ito Department of Computer Science and Engineering Graduate School of Engineering, Nagoya Institute of Technology Gokiso, Showa-ku, Nagoya 466-8555, Japan rafik@itolab.nitech.ac.jp, ito.takayuki@nitech.ac.jp |
| Pseudocode | No | The paper describes the message passing mechanism with equations (1a) and (1b) but does not present a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | We evaluated our model using parametrized random hypergraphs, showing that it can optimally handle complex utility spaces while outperforming previous sampling approaches. For the profile (40, [20, . . . , 100], π(Φj) 5 j) - The paper uses synthetically generated data and does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes generating data with specific profiles and parameters but does not specify train/validation/test splits or other details for data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers or other ancillary software details needed for replication. |
| Experiment Setup | No | The paper mentions 'propagation topologies' and 'profile' parameters for data generation, and discusses algorithmic variants like 'Asynch MPi', but does not provide specific hyperparameters or detailed system-level training settings for its experiments. |