Publication Details
Fuel in Markov Decision Processes (FiMDP): A Practical Approach to Consumption
Blahoudek František, RNDr., Ph.D.
CUBUKTEPE, M.
ORNIK, M.
THANGEDA, P.
TOPCU, U.
Behavioral research, Decision making, Model checking, Multi agent systems, Polynomial approximation
Consumption Markov Decision Processes (CMDPs) are probabilistic
decision-making models of resource-constrained systems. We introduce
FiMDP, a tool for controller synthesis in CMDPs with LTL objectives
expressible by deterministic Büchi automata. The tool implements the
recent algorithm for polynomial-time controller synthesis in CMDPs, but
extends it with many additional features. On the conceptual level, the
tool implements heuristics for improving the expected reachability times
of accepting states, and a support for multi-agent task allocation. On
the practical level, the tool offers (among other features) a new
strategy simulation framework, integration with the Storm model checker,
and FiMDPEnv - a new set of CMDPs that model real-world
resource-constrained systems. We also present an evaluation of FiMDP on
these real-world scenarios.
@inproceedings{BUT196648,
author="NOVOTNÝ, P. and BLAHOUDEK, F. and CUBUKTEPE, M. and ORNIK, M. and THANGEDA, P. and TOPCU, U.",
title="Fuel in Markov Decision Processes (FiMDP): A Practical Approach to Consumption",
booktitle="Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
year="2021",
pages="640--656",
address="Pittsburgh",
doi="10.1007/978-3-030-90870-6\{_}34",
isbn="978-3-030-90869-0",
url="https://link.springer.com/chapter/10.1007/978-3-030-90870-6_34?getft_integrator=scopus"
}