Fast SSP Solvers Using Short-Sighted Labeling
Authors: Luis Pineda, Kyle Wray, Shlomo Zilberstein
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we compare FLARES to an optimal algorithm, LRTDP, and two other short-sighted solvers, HDP(i,j) and SSi PP. We start by illustrating some of the advantages of FLARES over these approaches by applying them to a simple grid world problem that is easy to analyze. The remaining experiments, on the racetrack domain (Barto, Bradtke, and Singh 1995), the sailing domain (Kocsis and Szepesv ari 2006), and the triangle-tireworld domain (Little and Thiebaux 2007), aim to show that FLARES can perform consistently well across a variety of planning domains, in terms of solution quality and computation time. |
| Researcher Affiliation | Academia | Luis Pineda, Kyle Hollins Wray, Shlomo Zilberstein College of Information and Computer Sciences University of Massachusetts Amherst Amherst, MA 01003, USA {lpineda,wray,shlomo}@cs.umass.edu |
| Pseudocode | Yes | Algorithm 1: A depth limited procedure to label states. DLCHECKSOLVED" and "Algorithm 2: The FLARES algorithm. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links or explicit statements of code release) for the source code of the described methodology. |
| Open Datasets | Yes | We experimented with the racetrack domain described by (Barto, Bradtke, and Singh 1995)." and "We next present results on four instances of the sailing domain, described by (Kocsis and Szepesv ari 2006)." and "We experimented on the triangle-tireworld, a so-called probabilistically interesting problem (Little and Thiebaux 2007)... |
| Dataset Splits | No | The paper describes experiments on various domains but does not explicitly provide details about training, validation, or test dataset splits (e.g., percentages, sample counts, or specific pre-defined splits). |
| Hardware Specification | Yes | All algorithms were implemented by us and tested on a Intel Xeon 3.10 GHz computer with 16GB of RAM. |
| Software Dependencies | No | The paper mentions heuristics and planners used (e.g., 'mGPT planner', 'LRTA*'), but it does not specify software dependencies with concrete version numbers (e.g., 'Python 3.8', 'CPLEX 12.4'). |
| Experiment Setup | Yes | Unless otherwise specified, we used the following settings for our experiments: ... We used a value of ϵ 10^-3. ... All results are averaged over 100 runs of complete planning and execution simulations. |