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..
CoCoA: A Non-Iterative Approach to a Local Search (A)DCOP Solver
Authors: Cornelis Jan van Leeuwen, Przemyslaw Pawelczak
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, through evaluating graph coloring problems, randomized (A)DCOPs, and a sensor network communication problem, we show that Co Co A is able to very quickly find solutions of high quality with a smaller communication overhead than state-of-the-art DCOP solvers such as DSA, MGM-2, ACLS, MCS-MGM and Max-Sum. |
| Researcher Affiliation | Collaboration | Cornelis Jan van Leeuwen TNO and Delft University of Technology Eemsgolaan 3, Groningen, The Netherlands EMAIL Przemysław Pawełczak Delft University of Technology Mekelweg 4, Delft, The Netherlands EMAIL |
| Pseudocode | Yes | The proposed algorithm is given in pseudocode in Algorithm 1, with accompanying set of messages and agent states in Table 1 and Table 2, respectively and discussed in detail in the subsequent section. |
| Open Source Code | Yes | For a replicability of results and figures, the source code is available upon request, or at https://github.com/coenvl/m SAM. |
| Open Datasets | No | The paper describes how problems/graphs are generated for each experiment (e.g., 'The graphs are generated by selecting n = 500 random points in two dimensional space using a Poisson point process'), rather than using or providing access to a pre-existing public dataset. |
| Dataset Splits | No | The paper describes how experimental problems are generated and evaluated, but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | The experiments are carried out on a laptop with an Intel Core i7-3720 CPU 2.6 GHz and 8 GB RAM. |
| Software Dependencies | Yes | The solvers are implementated in Java 1.7, and the experiments are set up in Matlab 2015b, which is also used to post-process and present the result figures. |
| Experiment Setup | Yes | DSA (variant C, with update probability, p = 0.5), MGM-2 (Maheswaran, Pearce, and Tambe 2004) (with offer probability p = 0.5), Max-Sum ADVP (Zivan, Parash, and Naveh 2015) from hereon also referred to as simply Max-Sum, (switching graph direction after 100 iterations, value propagation after two switches, and using the constraint standard inner order), ACLS (with update probability p = 0.5) and MCS-MGM (Grubshtein et al. 2010) (non-parametric). |