Deceptive Path-Planning
Authors: Peta Masters, Sebastian Sardina
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 {peta.masters, sebastian.sardina}@rmit.edu.au |
| 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. |