scholarly journals ProMoca: Probabilistic Modeling and Analysis of Agents in Commitment Protocols

2016 ◽  
Vol 57 ◽  
pp. 465-508 ◽  
Author(s):  
Akın Günay ◽  
Yang Liu ◽  
Jie Zhang
Keyword(s):  
Model Checking ◽  
Multiagent Systems ◽  
Probabilistic Model ◽  
Model Uncertainty ◽  
Formal Language ◽  
Probabilistic Model Checking ◽  
Social Commitment ◽  
Modeling And Analysis ◽  
Essential Properties ◽  
Commitment Protocols

Social commitment protocols regulate interactions of agents in multiagent systems. Several methods have been developed to analyze properties of commitment protocols. However, analysis of an agent's behavior in a commitment protocol, which should take into account the agent's goals and beliefs, has received less attention. In this paper we present ProMoca framework to address this issue. Firstly, we develop an expressive formal language to model agents with respect to their commitments. Our language provides dedicated elements to define commitment protocols, and model agents in terms of their goals, behaviors, and beliefs. Furthermore, our language provides probabilistic and non-deterministic elements to model uncertainty in agents' beliefs. Secondly, we identify two essential properties of an agent with respect to a commitment protocol, namely compliance and goal satisfaction. We formalize these properties using a probabilistic variant of linear temporal logic. Thirdly, we adapt a probabilistic model checking algorithm to automatically analyze compliance and goal satisfaction properties. Finally, we present empirical results about efficiency and scalability of ProMoca.

scholarly journals Modeling and analysis of cell membrane systems with probabilistic model checking

BMC Genomics ◽  
2011 ◽  
Vol 12 (Suppl 4) ◽  
pp. S14 ◽  
Author(s):  
Mirlaine A Crepalde ◽  
Alessandra C Faria-Campos ◽  
Sérgio VA Campos
Keyword(s):  
Model Checking ◽  
Cell Membrane ◽  
Probabilistic Model ◽  
Probabilistic Model Checking ◽  
Membrane Systems ◽  
Modeling And Analysis

scholarly journals Probabilistic Model Checking and Autonomy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Marta Kwiatkowska ◽  
Gethin Norman ◽  
David Parker
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
Stochastic Games ◽  
Cooperative Behavior ◽  
Autonomous Systems ◽  
Probabilistic Model Checking ◽  
Annual Review ◽  
Publication Date ◽  
Modeling And Analysis ◽  
Markov Decision

The design and control of autonomous systems that operate in uncertain or adversarial environments can be facilitated by formal modeling and analysis. Probabilistic model checking is a technique to automatically verify, for a given temporal logic specification, that a system model satisfies the specification, as well as to synthesize an optimal strategy for its control. This method has recently been extended to multiagent systems that exhibit competitive or cooperative behavior modeled via stochastic games and synthesis of equilibria strategies. In this article, we provide an overview of probabilistic model checking, focusing on models supported by the PRISM and PRISM-games model checkers. This overview includes fully observable and partially observable Markov decision processes, as well as turn-based and concurrent stochastic games, together with associated probabilistic temporal logics. We demonstrate the applicability of the framework through illustrative examples from autonomous systems. Finally, we highlight research challenges and suggest directions for future work in this area. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Probabilistic Model Checking and Non-standard Multi-objective Reasoning

2014 ◽  
pp. 1-16 ◽  
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

scholarly journals A Railway Scheduling Method using Probabilistic Model Checking

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.

Evaluating probabilistic model checking tools for verification of robot control policies

2016 ◽  
Vol 29 (2) ◽  
pp. 287-299 ◽  
Author(s):  
Shashank Pathak ◽  
Luca Pulina ◽  
Armando Tacchella
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
Robot Control ◽  
Probabilistic Model Checking ◽  
Control Policies
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