A Unified Framework for Planning in Adversarial and Cooperative Environments
Authors: Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati2479-2487
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also present an empirical evaluation to show the feasibility and usefulness of our approaches using IPC domains. 5 Empirical Evaluation We now present an empirical analysis of all four approaches. |
| Researcher Affiliation | Academia | Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati School of Computing, Informatics, and Decision Systems Engineering Arizona State University, Tempe, AZ 85281 USA {anaghak, siddharths, rao} @ asu.edu |
| Pseudocode | Yes | Algorithm 1: Plan Computation |
| Open Source Code | No | We modified the STRIPS planner Pyperplan (Alkhazraji et al. 2016) to implement our algorithms. We used the hsa (Keyder and Geffner 2008) heuristic of Pyperplan because it gave the best results in terms of computation time. The paper does not state that their own code is open-source. |
| Open Datasets | Yes | We use three IPC domains, namely Blocksworld, Logistics and Driverlog to evaluate our approach. |
| Dataset Splits | No | No specific dataset split information (percentages, counts, or explicit standard splits for training/validation/testing) is provided. |
| Hardware Specification | Yes | We ran our experiments on 12 core Intel Xeon CPU with an E5-2643 v3@3.40GHz processor with a 64G RAM with 20 minutes time-out. |
| Software Dependencies | No | The paper mentions 'Pyperplan (Alkhazraji et al. 2016)' and 'hsa (Keyder and Geffner 2008) heuristic' but does not specify their version numbers or any other software dependencies with versions. |
| Experiment Setup | Yes | We ran the experiments with k = 3, j = 2, ℓ= 3, m = 3, dmin = 0.25 and dmax = 0.50 for all the domains. |