Anytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior
Authors: Panayiotis Danassis, Aris Filos-Ratsikas, Boi Faltings
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have evaluated ALMA under three test cases: (i) an anti-coordination scenario where agents with similar preferences compete over the same set of actions, (ii) a resource allocation scenario in an urban environment, under a constant-time constraint, and finally, (iii) an on-line matching scenario using real passenger-taxi data. In all of the cases, ALMA was able to reach high social welfare, while being orders of magnitude faster than the centralized, optimal algorithm. |
| Researcher Affiliation | Academia | Panayiotis Danassis , Aris Filos-Ratsikas and Boi Faltings Artificial Intelligence Laboratory, Ecole Polytechnique F ed erale de Lausanne (EPFL), Switzerland {panayiotis.danassis, aris.filosratsikas, boi.faltings}@epfl.ch |
| Pseudocode | Yes | Algorithm 1 ALMA: Altruistic Matching Heuristic. |
| Open Source Code | No | The paper does not provide a direct link to source code for the described methodology or explicitly state that the code is open-source or publicly available. |
| Open Datasets | Yes | We use a dataset2 of all taxi requests (ρ) in New York City during one week (34077 requests). The data include pickup and drop-off times, and geolocations. 2kaggle: /debanjanpaul/new-york-city-taxi-trip-distance-matrix/ |
| Dataset Splits | No | The paper evaluates ALMA under various test cases but does not provide specific training, validation, and test dataset splits (e.g., percentages, sample counts, or explicit standard split citations) to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Open Source Routing Machine (projectosrm.org)' but does not provide specific version numbers for this or any other software dependencies, making reproducible replication difficult. |
| Experiment Setup | Yes | In Section 3.1 we use the logistic function, while in 3.2 & 3.3 we use the linear function (Eq. 2) with ϵ = 0.1. [...] The parameters min W, max W, and q can be set by the ride-sharing company. We report results on different values for all of the above parameters. |