Planning for Stochastic Games with Co-Safe Objectives

Authors: Lei Song, Yuan Feng, Lijun Zhang

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We show that, if restricted to co-safe properties, a subset of PCTL properties capable to specify a wide range of properties in practice including reachability ones, the problem turns to be decidable, even when the class of general strategies is considered. We also give an algorithm for solving robust stochastic planning, where a winning strategy is tolerant to some perturbations of probabilities in the model. Our result indicates that satisfiability of co-safe PCTL is decidable as well.
Researcher Affiliation Academia Lei Song and Yuan Feng Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney, Australia Lijun Zhang State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China
Pseudocode No The paper refers to existing algorithms and states 'We also give an algorithm for solving robust stochastic planning', but it does not provide any structured pseudocode or algorithm blocks within its text.
Open Source Code No The paper does not contain any statement about releasing source code or provide links to a code repository for the described methodology.
Open Datasets No The paper is theoretical and does not involve the use of datasets for training or evaluation, therefore no information about public dataset availability is provided.
Dataset Splits No The paper is theoretical and does not involve experimental data, therefore it does not provide information about training/validation/test splits.
Hardware Specification No The paper is theoretical and does not describe any experiments, therefore no hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and does not describe any experiments that would require listing software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations.