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 [1].
Reconnection with the Ideal Tree: A New Approach to Real-Time Search
Authors: N. Rivera, L. Illanes, J. A. Baier, C. Hernandez
JAIR 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach over grid pathfinding benchmarks including game maps and mazes. Our results show that FRIT, used with RTAA*, a standard RTHS algorithm, outperforms RTAA* significantly; by one order of magnitude under tight time constraints. In addition, FRIT(da RTAA*) substantially outperforms da RTAA*, a state-of-the-art RTHS algorithm, usually obtaining solutions 50% cheaper on average when performing the same search effort. Finally, FRIT(BFS), i.e., FRIT using breadth-first-search, obtains best-quality solutions when time is limited compared to Adaptive A* and Repeated A*. Finally we show that Bug2, a pathfinding-specific navigation algorithm, outperforms FRIT(BFS) when planning time is extremely limited, but when given more time, the situation reverses. |
| Researcher Affiliation | Academia | Nicol as Rivera EMAIL King s College London London, WC2R 2LS, UK Le on Illanes EMAIL Jorge A. Baier EMAIL Departamento de Ciencia de la Computaci on Pontificia Universidad Cat olica de Chile Vicu na Mackenna 4860, Santiago, Chile Carlos Hern andez EMAIL Departamento de Ingenier ıa Inform atica Universidad Cat olica de la Sant ısima Concepci on Caupolic an 491, Concepci on, Chile |
| Pseudocode | Yes | Algorithm 1: A Generic Real-Time Search Algorithm; Algorithm 2: RTAA* s heuristic learning.; Algorithm 3: FRIT: Follow and Reconnect with The Ideal Tree; Algorithm 4: Reconnect component of FRIT; Algorithm 5: In Tree[c] function |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions that videos can be viewed at a URL, but this is not for source code. |
| Open Datasets | Yes | We used twelve maps from deployed video games and four different mazes. Both the maps and the mazes were retrieved from Nathan Sturtevant s pathfinding repository (Sturtevant, 2012). |
| Dataset Splits | No | The paper states: "We averaged our experimental results over 500 test cases with a reachable goal cell for each map. For each test case the start and goal cells were chosen randomly." This describes how experimental instances were generated but does not provide specific training/testing/validation splits for a dataset in a machine learning context. |
| Hardware Specification | Yes | All the experiments were run on a 2.00GHz Quad Core Intel Xeon machine running Linux. |
| Software Dependencies | No | The paper mentions "Linux" as the operating system but does not provide specific version numbers for any software libraries, programming languages, or solvers used in the experiments. |
| Experiment Setup | Yes | All real-time algorithms were run with 10 different parameter values. The incremental algorithms were run to completion once per test case, after which the results were processed to show the behavior corresponding to using 150,000 different values for the k parameter. For the case of FRIT(BFS), we satisfy the real-time property as discussed above by setting both constants, NE and NT , to 1. We averaged our experimental results over 500 test cases with a reachable goal cell for each map. For each test case the start and goal cells were chosen randomly. |