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..
Efficient Object Search in Game Maps
Authors: Jinchun Du, Bojie Shen, Shizhe Zhao, Muhammad Aamir Cheema, Adel Nadjaran Toosi
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experimental study, conducted on standard game maps benchmarks and real-world keywords, demonstrates that our approach has up to 2 orders of magnitude faster update times for moving objects compared to stateof-the-art approaches such as navigation mesh and IR-tree. |
| Researcher Affiliation | Academia | Jinchun Du , Bojie Shen , Shizhe Zhao , Muhammad Aamir Cheema , Adel Nadjaran Toosi Faculty of Information Technology, Monash University, Melbourne, Australia EMAIL |
| Pseudocode | Yes | Algorithm 1: Boolean k NN query processing |
| Open Source Code | Yes | 1https://github.com/goldi1027/GT-EHL |
| Open Datasets | Yes | We run experiments on widely used game map benchmarks [Sturtevant, 2012] of four popular games: Dragon Age II (DA); Dragon Age Origins (DAO); Baldur s Gate II (BG) and Star Craft (SC). |
| Dataset Splits | No | The paper describes generating objects, keywords, and queries, but does not specify formal training, validation, or test dataset splits or percentages. |
| Hardware Specification | Yes | We run our experiments on a 3.2 GHz Intel Core i7 machine with 32 GB of RAM. |
| Software Dependencies | No | All the algorithms are implemented in C++ and compiled with -O3 flag. The paper mentions using 'nltk, an NLP library' but does not specify its version. It also mentions 'Chat GPT (Jan 9 version)' but this is a tool used for keyword generation, not a software dependency for the algorithm itself. |
| Experiment Setup | Yes | We vary the density from 0.1% to 10% and the default density is 1%. We define mobility of an object set as the percentage of objects that move between two timestamps. We vary the mobility from 10% to 100% and the default mobility is 70%. We evaluate the effect of k which is varied from 1 to 10 where the default value of k is 3. We also evaluate the effect of number of query keywords by varying the number of query keywords from 0 to 3 where the default number of keywords is 2. For each experiment, we generate 100 queries per timestamp. |