A Combinatorial Search Perspective on Diverse Solution Generation
Authors: Satya Gautam Vadlamudi, Subbarao Kambhampati
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the efficacy of the proposed framework compared to an existing greedy approach. |
| Researcher Affiliation | Academia | Satya Gautam Vadlamudi and Subbarao Kambhampati School of Computing, Informatics, and Decision Systems Engineering Arizona State University {gautam , rao}@asu.edu |
| Pseudocode | Yes | Algorithm 1 presents a simple strategy called ACER; Algorithm 2 presents the proposed method MCER; Algorithm 3 presents the pseudo-code of Expand Most Promising routine. |
| Open Source Code | No | The paper mentions implementing methods on top of an existing planning environment but does not state that the code for the described methodology is open source or provide a link. |
| Open Datasets | No | The paper mentions using standard planning domains ('Blocks', 'Rovers', 'Zeno-Travel') but does not provide specific access information (URL, DOI, citation with authors/year) for these datasets, only for the Fast Downward planning environment tool. |
| Dataset Splits | No | The paper does not specify exact percentages, sample counts, or reference predefined splits for training, validation, or test sets. |
| Hardware Specification | Yes | All the experiments have been performed on a machine with Intel(R) Xeon(R) CPU E5-1620 v2 at 3.70GHz and 64GB RAM. |
| Software Dependencies | No | We have implemented all our methods on top of the Fast Downward planning environment (Helmert 2006). The paper names the software but does not provide a specific version number. |
| Experiment Setup | Yes | Table 2 presents the comparison of DFAM and A*AM methods (with plan-combinations seed set size equal to 30 in each execution). We have used the LMcut heuristic which is admissible, to guide the search. |