Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Probabilistic Inference Based Message-Passing for Resource Constrained DCOPs
Authors: Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on standard benchmarks show that our approach provides better quality than previous best DCOP algorithms and has much lower failure rate. |
| Researcher Affiliation | Academia | Supriyo Ghosh School of Info. Systems Singapore Management Univ. EMAIL Akshat Kumar School of Info. Systems Singapore Management Univ. EMAIL Pradeep Varakantham School of Info. Systems Singapore Management Univ. EMAIL |
| Pseudocode | Yes | All the steps of the EM and the BCD approach can be implemented via message-passing over the RACN for a RC-DCOP as shown in Alg. 1 and 2. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper describes generating synthetic data ('random graphs' and 'graph coloring') based on various parameters for its experiments, rather than using a publicly available dataset with a specific access link or formal citation. |
| Dataset Splits | No | The paper does not explicitly mention training, validation, or test dataset splits. It describes generating problem instances for DCOPs for evaluation rather than using pre-defined splits of a fixed dataset. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'MS implementation provided by the Frodo 2.0 software [Leaut e et al., 2009]' and 'Our approach was implemented in Python' but does not provide specific version numbers for these software dependencies (e.g., Python 3.x, Frodo 2.0.x). |
| Experiment Setup | Yes | We tested with 30 and 40 node graphs with domain size |Di|=5. We vary the edge density from 0.5 to 0.9... Each utility, θij( , ), is a random value between 1 and 10. Each resource constraint involved three agents... The resource consumption of agents for each resource was also generated randomly between 1 to 5. We controlled the resource capacity C(r) of each resource carefully. Let Mr, mr denote the maximum and minimum amount of resource r respectively that can be consumed by all the involved agents. To control the tightness of capacity constraints, we use a parameter tr varied from 0.2 to 0.6. The capacity C(r) is set as mr+tr(Mr mr). |