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..
Preserving Privacy in Region Optimal DCOP Algorithms
Authors: Tamir Tassa, Roie Zivan, Tal Grinshpoun
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The estimations are backed with experimental results. ... Figure 1 depicts the computational overhead as a function of k in the three network types... Figure 2 confirms that the problem’s size has almost no effect on the computational overhead... |
| Researcher Affiliation | Academia | Tamir Tassa The Open University ... Roie Zivan Ben-Gurion University of the Negev ... Tal Grinshpoun Ariel University |
| Pseudocode | Yes | Protocol 1 describes RODA, an algorithmic framework that generalizes existing region-optimal algorithms [Katagishi and Pearce, 2007; Kiekintveld et al., 2010; Vinyals et al., 2011]. ... Protocol 1 RODA: 1: The agents exchange local information (see details in the text). 2: Ai randomly selects a0i 2 Di, 1 i n. 3: for = 1, . . . , L do 4: Ai sets ai , 1 i n. 5: Ah selects A~h 2 Rh, 1 h n. 6: Ah receives a 1j from Aj for all Aj 2 S~h 7: Ah finds a locally optimal tuple of assignments, β 2 D~h Di, for the variables controlled by its group A~h. 8: Ah computes ∆h, the cost improvement if all agents in A~h update their assignment to the one given by β. 9: Ah informs all agents in A~h about the found β and ∆h. 10: For every 1 h n: If A~h wins the contest against A~h0 for all groups A~h0 that are neighboring to A~h, then 8Ai 2 A~h, Ai sets ai according to β. (Winning occurs if ∆h > ∆h0 and h < h0.) 11: Ai sets Xi := aL i. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | The paper describes generating synthetic network types (random, scale-free, small-world) for its experiments and fixes parameters like number of agents and constraint density, but it does not use or provide access to a public or open dataset. |
| Dataset Splits | No | The paper describes parameters for generating synthetic networks for simulation (e.g., n=100, p=0.1, d=5, t=1, L=50 iterations) but does not define any training, validation, or test dataset splits. |
| Hardware Specification | Yes | Our tests show that encryption takes at most CE = 2 msec, while decryption takes at most CD = 3 msec. by averaging multiple runs of the common Java implementation of the Paillier cryptosystem2 on a hardware comprised of an Intel i7-4600U processor and 16GB memory. |
| Software Dependencies | No | The paper mentions 'Java implementation of the Paillier cryptosystem' and provides a URL for the Paillier implementation, but it does not specify version numbers for Java or the cryptosystem library itself in the text. |
| Experiment Setup | Yes | In this setup we fix the number of agents to n = 100, the constraint density to p = 0.1, the domain sizes to d = 5, and the upper bound of the distance to t = 1, and vary k = 3, . . . , 8. ... when running the algorithm for L = 50 iterations. |