CoCoA: A Non-Iterative Approach to a Local Search (A)DCOP Solver
Authors: Cornelis Jan van Leeuwen, Przemyslaw Pawelczak
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 coen.vanleeuwen@tno.nl Przemysław Pawełczak Delft University of Technology Mekelweg 4, Delft, The Netherlands p.pawelczak@tudelft.nl |
| 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). |