Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
SLIM: Semi-Lazy Inference Mechanism for Plan Recognition
Authors: Reuth Mirsky, Ya’akov (Kobi) Gal
IJCAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,166 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,378 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show both theoretically and empirically that although the computational cost of this process is still exponential, there is a significant improvement in run-time when compared to a state of the art of plan recognition algorithm. We evaluated the SLIM algorithm on a simulated domain, based on AND/OR trees used by Kabanza et al. [2013]. We used their same configuration which includes 100 instances with a fixed number (100) of basic actions, five identified goals, a branching factor of 3 for AND rules in the grammar and a branching factor of 2 for OR rules. |
| Researcher Affiliation | Academia | Reuth Mirsky and Ya akov (Kobi) Gal Department of Information Systems Engineering Ben-Gurion University of the Negev, Israel {EMAIL,EMAIL} |
| Pseudocode | Yes | Figure 2: SLIM s Main Functions, which contains pseudocode blocks for 'function SLIM BOTTOM-UP', 'function COMBINEDIRECTLY', and 'function COMBINEASSIBLING'. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | We evaluated the SLIM algorithm on a simulated domain, based on AND/OR trees used by Kabanza et al. [2013]. |
| Dataset Splits | No | The paper states '100 instances' and 'Each instance contains a sequence of 9 basic actions' for the simulated domain, but does not provide specific train, validation, or test dataset splits. |
| Hardware Specification | No | The paper states 'These tests were conducted on the same commodity computer' but does not provide specific details on the hardware used, such as GPU/CPU models, processor types, or memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for replication, such as programming language versions or library versions. |
| Experiment Setup | Yes | We used their same configuration which includes 100 instances with a fixed number (100) of basic actions, five identified goals, a branching factor of 3 for AND rules in the grammar and a branching factor of 2 for OR rules. |