Result 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.
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.
Behavioral research, Decision making, Model checking, Multi agent systems, Polynomial approximation
@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"
}