Scrubbing During Learning In Real-time Heuristic Search
Authors: Nathan R. Sturtevant, Vadim Bulitko
JAIR 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then show that while the asymptotic analysis does not hold for more complex realtime search algorithms, experimental results suggest that it is still descriptive of practical performance. The theoretical results are supported by a brief empirical evaluation in Section 6. |
| Researcher Affiliation | Academia | Nathan R. Sturtevant EMAIL Department of Computer Science University of Denver Denver, Colorado, USA Vadim Bulitko EMAIL Department of Computing Science University of Alberta Edmonton, Alberta, T6G 2E8, Canada |
| Pseudocode | Yes | Algorithm 1: Basic Real-time Heuristic Search input : search problem (S, E, s0, sg, h), tie-breaking schema τ output: path (s0, s1, . . . , s T ), s T = sg 1 t 0 3 while st = sg do 4 st+1 arg minτ s N(st)(1 + ht(s )) 5 ht+1(st) 1 + ht(st+1) ... Algorithm 2: Real-time Heuristic Search with Lookahead input : search problem (S, E, s0, sg, h), lookahead l output: path (s0, s1, . . . , s T ), s T = sg 1 t 0 3 while st = sg do 4 OPEN {st} |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. There are no explicit statements about code release, repository links, or supplementary materials containing code. |
| Open Datasets | Yes | We performed these experiments on the Moving AI benchmark set (Sturtevant, 2012), on all Dragon Age: Origins maps with the optimal solution cost in [400, 404). A total of 600 problems were used over 60 maps. |
| Dataset Splits | No | The paper describes using a total of 600 problems over 60 maps for the video-game maps and 1100 problems for the maze maps, selected based on optimal solution cost. However, it does not provide specific training/test/validation dataset splits, percentages, or sample counts for model training or evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | For each value of n {10, 11, . . . , 100} (i.e., n n map with Θ(n2) states), we ran 20 trials of LRTA* with lookahead of 1 and random tie breaking (instead of τ). ... In Figure 17(b) we look at the performance of g LSS-LRTA* on the same corner map with lookahead 1 and 10. ... LSS-LRTA* (Koenig & Sun, 2009) with lookahead depths of 1, 10 and 100 (LSS-LRTA* with lookahead 1 is equivalent to Algorithm 1). Diagonal movement was allowed with cost 1.5. Ties were broken in a fixed way in each state, according to the operator ordering and data structures, without randomization. |