Approximability of Constant-horizon Constrained POMDP
Authors: Majid Khonji, Ashkan Jasour, Brian Williams
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our first contribution is a reduction from CC-POMDP to C-POMDP and a novel Integer Linear Programming (ILP) formulation. Thus, any algorithm for the later problem can be utilized to solve any instance of the former. Second, we show that unlike POMDP, when the length of the planning horizon is constant, (C)C-POMDP is NP-Hard. Third, we present the first Fully Polynomial Time Approximation Scheme (FPTAS) that computes (near) optimal deterministic policies for constant-horizon (C)C-POMDP in polynomial time. |
| Researcher Affiliation | Academia | 1EECS Department, KU Center for Autonomous Robotic Systems, Khalifa University, Abu Dhabi, UAE 2Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA |
| Pseudocode | Yes | Algorithm 1 MPKP-FPTAS[(uj, rj)j ∈ A, C, G, ϵ] |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper focusing on mathematical formulations, complexity analysis, and algorithm design. It does not use or reference any datasets, public or otherwise, for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments. Consequently, it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments. Therefore, it does not specify any hardware used for computations or experiments. |
| Software Dependencies | No | The paper describes theoretical algorithms and formulations. While it mentions concepts like ILP solvers, it does not specify any software dependencies with version numbers for running experiments or computations related to its research. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments. Therefore, it does not provide details on experimental setup, hyperparameters, or system-level training settings. |