Integration of Planning with Recognition for Responsive Interaction Using Classical Planners
Authors: Richard Freedman, Shlomo Zilberstein
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that, like the used recognition method, these interaction problems can be compiled into classical planning problems and solved using offthe-shelf methods. In addition to the methodology, this paper introduces problem categories for different forms of interaction, an evaluation metric for the benefits from the interaction, and extensions to the recognition algorithm that make its intermediate results more practical while the plan is in progress. |
| Researcher Affiliation | Academia | Richard G. Freedman and Shlomo Zilberstein College of Information and Computer Sciences University of Massachusetts Amherst, MA 01003, USA {freedman,shlomo}@cs.umass.edu |
| Pseudocode | No | The paper provides mathematical definitions and formalisms but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code related to the described methodology. |
| Open Datasets | Yes | To demonstrate the process of responsive interaction, we create a problem based on the Block-words domain from Ram ırez and Geffner s (2010) dataset. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. It uses an illustrative example scenario rather than a dataset with predefined splits. |
| Hardware Specification | No | The paper does not specify any hardware used for running experiments (e.g., GPU, CPU models, memory). |
| Software Dependencies | No | The paper mentions 'Fast-Downward planner (Helmert 2006)' but does not provide a specific version number for the software used, only the year of the associated paper. |
| Experiment Setup | Yes | We will use ϵ = 3 because it maximizes the necessities, but the threshold τ = 0.3 (slightly less than the greatest probability given to a goal at the beginning) yields the same propositions for all ϵ 5. |