Efficient, Safe, and Probably Approximately Complete Learning of Action Models
Authors: Roni Stern, Brendan Juba
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we explore the theoretical boundaries of planning in a setting where no model of the agent s actions is given. Instead of an action model, a set of successfully executed plans are given and the task is to generate a plan that is safe, i.e., guaranteed to achieve the goal without failing. To this end, we show how to learn a conservative model of the world in which actions are guaranteed to be applicable. This conservative model is then given to an off-the-shelf classical planner, resulting in a plan that is guaranteed to achieve the goal. However, this reduction from a model-free planning to a model-based planning is not complete: in some cases a plan will not be found even when such exists. We analyze the relation between the number of observed plans and the likelihood that our conservative approach will indeed fail to solve a solvable problem. Our analysis show that the number of trajectories needed scales gracefully. |
| Researcher Affiliation | Academia | Roni Stern Ben Gurion University of the Negev Be er Sheva, Israel roni.stern@gmail.com Brendan Juba Washington University in St. Louis St. Louis, MO, 63130 USA bjuba@wustl.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with a specific dataset. It refers to a "set of trajectories" in a general sense for its theoretical model, not a publicly available dataset for evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with specific dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not specify any hardware used for computations or experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training settings. |