Probabilistic Inference Based Message-Passing for Resource Constrained DCOPs
Authors: Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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. supriyog.2013@phdis.smu.edu.sg Akshat Kumar School of Info. Systems Singapore Management Univ. akshatkumar@smu.edu.sg Pradeep Varakantham School of Info. Systems Singapore Management Univ. pradeepv@smu.edu.sg |
| 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). |