Stochastic Goal Recognition Design Problems with Suboptimal Agents
Authors: Christabel Wayllace, William Yeoh9953-9961
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluating our approach on a range of benchmark applications. ... Empirical Evaluation The objective of this section is to evaluate the usefulness and scalability of our methods. We describe the settings used and present and analyze the experimental results. |
| Researcher Affiliation | Academia | Christabel Wayllace1, William Yeoh2 1 University of Alberta 2 Washington University in St. Louis |
| Pseudocode | Yes | Procedure aug MDP-PO(ρ, , s0, S, Π G, T , N) 1 Stack ; S ; i o , T null, G ; O null ... 12 return i, S , T , G |
| Open Source Code | Yes | The source code is available at https://github.com/cwayllace/SS-GRD. |
| Open Datasets | Yes | Data: We evaluated our approach on five modified planning domains: (1) GRID-NAVIGATION, (2) ROOM, (3) BLOCKSWORLD, (4) BOXWORLD, and (5) ATTACK-PLANNING (further details in the appendix). |
| Dataset Splits | No | The paper describes experimental configurations but does not specify data splits like training, validation, or test sets in the context of machine learning models. |
| Hardware Specification | Yes | Experiments were conducted on a 2.10 GHz machine with 16 GB of RAM and a timeout of 52 hours. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We used a budget k of up to 3 modifications and allowed up to 2 suboptimal actions (u = 1 and u = 2). |