Scalable Greedy Algorithms for Task
Authors: Pritee Agrawal, Pradeep Varakantham, William Yeoh
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate2 our GAPS and LDD+GAPS algorithms on two benchmark problems from the literature. We compare both our approaches with an optimal MILP by Dolgov and Durfee [2006] (referred to as Optimal MILP), solved using CPLEX, which is a state-of-the-art algorithm for multi-agent coordination. We evaluate the performance and scalability of our approaches in reference to the optimal MILP in MADP and UCC domains. |
| Researcher Affiliation | Academia | Pritee Agrawal Singapore Management University Singapore 188065 priteea.2013@phdis.smu.edu.sg Pradeep Varakantham Singapore Management Univ. Singapore 188065 pradeepv@smu.edu.sg William Yeoh New Mexico State Univ. Las Cruces, NM 88003 USA wyeoh@cs.nmsu.edu |
| Pseudocode | Yes | Algorithm 1 GAPS(Tas C MDP) |
| Open Source Code | No | The paper does not include any statement about making its source code available or provide a link to a code repository. |
| Open Datasets | Yes | The first domain is Multi-Agent Delivery Problems (MADPs) that have transitional uncertainty but without resource dependencies [Dolgov and Durfee, 2006]. The second domain is Urban Consolidation Center (UCC) problems that deal with allocation of tasks and has transitional uncertainty along with task dependencies [Handoko et al., 2014; Wang et al., 2014]. |
| Dataset Splits | No | The paper describes experiments on 'benchmark problems' and 'randomly generated grid maps' but does not specify distinct training, validation, and test splits with percentages or sample counts. The evaluation is done on generated instances. |
| Hardware Specification | No | The paper states 'All our optimization problems are run on CPLEX v12.5.' but does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types. |
| Software Dependencies | Yes | All our optimization problems are run on CPLEX v12.5. |
| Experiment Setup | Yes | Experimental results are averaged over 15 randomly generated grid maps... A time cut-off limit of two hours was used for Optimal MILP... Each agent i has a fixed limited budget ˆqi of 6 resources to perform its tasks. |