Integrating Planning and Recognition to Close the Interaction Loop
Authors: Richard Freedman
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | I ran LDA on a small dataset. The results provided evidence supporting my hypothesis since each topic contained postures resembling simple actions as in Figure 2 (Freedman, Jung, and Zilberstein 2014). I developed a simulator for a multiplayer version of the Sokoban game (a benchmark in planning research) and we will begin implementing and testing these methods within the next two months. |
| Researcher Affiliation | Academia | Richard G. Freedman College of Information and Computer Sciences University of Massachusetts Amherst freedman@cs.umass.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific repository links or explicit statements about code availability for the methodology described. |
| Open Datasets | No | The paper mentions running LDA 'on a small dataset' and developing a simulator for the 'Sokoban game', but does not provide concrete access information (link, DOI, citation, or repository) for any publicly available or open dataset used. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Latent Dirichlet Allocation (LDA) topic model (Blei, Ng, and Jordan 2003)' but does not provide specific version numbers for any software libraries, frameworks, or solvers used. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values or training configurations. |