Synthesis method of strategy for multi-robot systems from local automatons under probabilistic model checking

Through innovatively introducing the receding horizon into probabilistic model checking, an online strategy synthesis method for multi-robot systems from local automatons is proposed to complete complex tasks that are assigned to each robot. Firstly, each robot is modeled as a Markov decision process which models both probabilistic and nondeterministic behavior. Secondly, the task specification of each robot is expressed as a linear temporal logic formula. For some tasks that robots cannot complete by themselves, the collaboration requirements take the form of atomic proposition into the LTL specifications. And the LTL specifications are transformed to deterministic rabin automatons over which a task progression metric is defined to determine the local goal states in the finite-horizon product systems. Thirdly, two horizons are set to determine the running steps in automatons and MDPs. By dynamically building local finite-horizon product systems, the collaboration strategies are synthesized iteratively for each robot to satisfy the task specifications with maximum probability. Finally, through simulation experiments in an indoor environment, the results show that the method can synthesize correct strategies online for multi-robot systems which has no restriction on the LTL operators and reduce the computational burden brought by the automaton-based approach.

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Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

Computer Aided Verification - Lecture Notes in Computer Science ◽

10.1007/978-3-030-81688-9_27 ◽

2021 ◽

pp. 577-601

Author(s):

Steven Holtzen ◽

Sebastian Junges ◽

Marcell Vazquez-Chanlatte ◽

Todd Millstein ◽

Sanjit A. Seshia ◽

...

Keyword(s):

Markov Chain ◽

Model Checking ◽

Markov Chains ◽

Probabilistic Model ◽

State Of The Art ◽

Probabilistic Inference ◽

Finite Horizon ◽

Probabilistic Model Checking ◽

Valuable Addition ◽

Bounded State

AbstractWe revisit the symbolic verification of Markov chains with respect to finite horizon reachability properties. The prevalent approach iteratively computes step-bounded state reachability probabilities. By contrast, recent advances in probabilistic inference suggest symbolically representing all horizon-length paths through the Markov chain. We ask whether this perspective advances the state-of-the-art in probabilistic model checking. First, we formally describe both approaches in order to highlight their key differences. Then, using these insights we develop Rubicon, a tool that transpiles Prism models to the probabilistic inference tool . Finally, we demonstrate better scalability compared to probabilistic model checkers on selected benchmarks. All together, our results suggest that probabilistic inference is a valuable addition to the probabilistic model checking portfolio, with Rubicon as a first step towards integrating both perspectives.

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Modelling and verification of reconfigurable multi-agent systems

Autonomous Agents and Multi-Agent Systems ◽

10.1007/s10458-021-09521-x ◽

2021 ◽

Vol 35 (2) ◽

Author(s):

Yehia Abd Alrahman ◽

Nir Piterman

Keyword(s):

Model Checking ◽

Communication Protocols ◽

Local State ◽

Multi Agent Systems ◽

Agent Systems ◽

Robot Systems ◽

Message Exchange ◽

Multi Agent ◽

Exchange Data ◽

Multi Robot

AbstractWe propose a formalism to model and reason about reconfigurable multi-agent systems. In our formalism, agents interact and communicate in different modes so that they can pursue joint tasks; agents may dynamically synchronize, exchange data, adapt their behaviour, and reconfigure their communication interfaces. Inspired by existing multi-robot systems, we represent a system as a set of agents (each with local state), executing independently and only influence each other by means of message exchange. Agents are able to sense their local states and partially their surroundings. We extend ltl to be able to reason explicitly about the intentions of agents in the interaction and their communication protocols. We also study the complexity of satisfiability and model-checking of this extension.

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Comprehensive vulnerability analysis of systems exposed to SEUs via probabilistic model checking

2016 16th European Conference on Radiation and Its Effects on Components and Systems (RADECS) ◽

10.1109/radecs.2016.8093191 ◽

2016 ◽

Cited By ~ 2

Author(s):

Marwan Ammar ◽

Ghaith Bany Hamad ◽

Otmane Ait Mohamed ◽

Yvon Savaria ◽

Raoul Velazco

Keyword(s):

Model Checking ◽

Probabilistic Model ◽

Vulnerability Analysis ◽

Probabilistic Model Checking

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Dynamic Power Management of a System With a Two-Priority Request Queue Using Probabilistic-Model Checking

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ◽

10.1109/tcad.2007.911342 ◽

2008 ◽

Vol 27 (2) ◽

pp. 403-407 ◽

Cited By ~ 12

Author(s):

Aleksandra Sesic ◽

Stanisa Dautovic ◽

Veljko Malbasa

Keyword(s):

Model Checking ◽

Power Management ◽

Probabilistic Model ◽

Probabilistic Model Checking ◽

Dynamic Power Management ◽

Dynamic Power ◽

Request Queue

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Infer Gene Regulatory Networks from Time Series Data with Probabilistic Model Checking

2015 IEEE/ACM 3rd FME Workshop on Formal Methods in Software Engineering ◽

10.1109/formalise.2015.12 ◽

2015 ◽

Cited By ~ 9

Author(s):

Michele Ceccarelli ◽

Luigi Cerulo ◽

Giuseppe De Ruvo ◽

Vittoria Nardone ◽

Antonella Santone

Keyword(s):

Time Series ◽

Model Checking ◽

Gene Regulatory Networks ◽

Probabilistic Model ◽

Regulatory Networks ◽

Time Series Data ◽

Series Data ◽

Probabilistic Model Checking ◽

Gene Regulatory

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Ontology-Mediated Probabilistic Model Checking

Lecture Notes in Computer Science - Integrated Formal Methods ◽

10.1007/978-3-030-34968-4_11 ◽

2019 ◽

pp. 194-211 ◽

Cited By ~ 1

Author(s):

Clemens Dubslaff ◽

Patrick Koopmann ◽

Anni-Yasmin Turhan

Keyword(s):

Model Checking ◽

Probabilistic Model ◽

Probabilistic Model Checking

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Probabilistic Model Checking and Non-standard Multi-objective Reasoning

Fundamental Approaches to Software Engineering - Lecture Notes in Computer Science ◽

10.1007/978-3-642-54804-8_1 ◽

2014 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Christel Baier ◽

Clemens Dubslaff ◽

Sascha Klüppelholz ◽

Marcus Daum ◽

Joachim Klein ◽

...

Keyword(s):

Model Checking ◽

Probabilistic Model ◽

Probabilistic Model Checking ◽

Multi Objective ◽

Objective Reasoning

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A Railway Scheduling Method using Probabilistic Model Checking

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1980.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 6694-6698

Keyword(s):

Model Checking ◽

Probabilistic Model ◽

Probabilistic Model Checking ◽

Railway Transportation ◽

Railway Network ◽

Double Line ◽

Arrival Times ◽

Scheduling Method ◽

Prism Model ◽

The Impact

Trains scheduling is an important problem in railway transportation. Many companies use fixed train timetabling to handle this problem. Train delays can affect the pre-defined timetables and postpone destination arrival times. Besides, delay propagation may affect other trains and degrade the performance of a railway network. An optimal timetable minimizes the total propagated delays in a network. In this paper, we propose a new approach to compute the expected propagated delays in a railway network. As the main contribution of the work, we use Discrete-time Markov chains to model a railway network with a fixed timetable and use probabilistic model checking to approximate the expected delays and the probability of reaching destinations with a desired delay. We use PRISM model checker to apply our approach for analyzing the impact of different train scheduling in double line tracks.

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