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 [1].
HS-CAI: A Hybrid DCOP Algorithm via Combining Search with Context-Based Inference
Authors: Dingding Chen, Yanchen Deng, Ziyu Chen, Wenxing Zhang, Zhongshi He7087-7094
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, (iii) we prove the correctness of our algorithm and the experimental results demonstrate its superiority over the state-of-the-art. Empirical Evaluation In this section, we ο¬rst investigate the effect of the parameter t in the context evaluation mechanism on HS-CAI. Then, we present the experimental comparisons of HS-CAI with stateof-the-art complete DCOP algorithms. |
| Researcher Affiliation | Academia | Dingding Chen,1 Yanchen Deng,2 Ziyu Chen,1, Wenxing Zhang,1 Zhongshi He1 1College of Computer Science, Chongqing University, Chongqing, China 2School of Computer Science and Engineering, Nanyang Technological University, Singapore EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Hybrid phase for agent ai |
| Open Source Code | Yes | All evaluated algorithms are implemented in DCOPSovler1, the DCOP simulator developed by ourselves. 1https://github.com/czy920/DCOPSovler |
| Open Datasets | No | For each experiment, we generate 50 random instances and report the average of over all instances. |
| Dataset Splits | No | The paper describes generating "50 random instances" but does not specify how these instances were split into training, validation, or test sets. |
| Hardware Specification | No | The paper does not specify any hardware details used for the experiments. |
| Software Dependencies | No | All evaluated algorithms are implemented in DCOPSovler1, the DCOP simulator developed by ourselves. |
| Experiment Setup | Yes | Therefore, we set t = (dmax)Οh. Moreover, we choose k = 6 and k = 10 as the low and high memory budget for MB-DPOP, HS-AI, HS-CAI(-M) and HS-CAI. Thus, we choose Ο to 0.25 for HS-CAI(k = 6) and 0.45 for HS-CAI(k = 10) in the following comparison experiments. |