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..
Deceptive Path-Planning
Authors: Peta Masters, Sebastian Sardina
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide an empirical evaluation, review related work and present our conclusion. and Figure 4: Results show the percentage of paths returned by each strategy that were deceptive when tested at 10%, 25%, etc., of their path length prior to the RMP (beyond the RMP, all paths are truthful). Table columns show average (total) path costs and average time taken to generate the (total) path. |
| Researcher Affiliation | Academia | Peta Masters and Sebastian Sardina RMIT University, Melbourne, Australia EMAIL |
| Pseudocode | No | The paper discusses formulas and strategies but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We generated a problem set based on game maps from the Moving-AI benchmarks [Sturtevant, 2012] |
| Dataset Splits | No | The paper states 'For each of 50 problems, we generated one optimal path using a standard implementation of A* and four deceptive paths (each using a different strategy),' but it does not specify any explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | Experiments were conducted on a i7 3.6GHz machine with 8GB RAM. |
| Software Dependencies | No | The paper mentions 'a standard implementation of A*' and 'Ramirez and Geffner s method of goal recognition,' but does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We generated a problem set based on game maps from the Moving-AI benchmarks [Sturtevant, 2012] to which we added three extra candidate goals at random locations. For each of 50 problems, we generated one optimal path using a standard implementation of A* and four deceptive paths (each using a different strategy). We timed path generation and recorded path costs. We truncated paths at the RMP (beyond which all paths would be truthful) and, using Ramirez and Geffner s method of goal recognition, calculated probabilities at intervals to confirm/assess deceptive density and extent. and if h(n, gr) < h(n, gmin) then h(n, t) = αh(n, t), where constant α > 1. |