Momba: JANI Meets Python

Köhl, Maximilian A.; Klauck, Michaela; Hermanns, Holger

doi:10.1007/978-3-030-72013-1_23

Cited by 4 publications

(1 citation statement)

References 26 publications

(31 reference statements)

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“…For (a), it implements and extends the OpenAI Gym API [19]. MoGym is based on Momba [74], a Python toolbox for dealing with quantitative models from construction to analysis centred around JANI. MoGym can process JANI models for the description of a training environment and, based on the induced formal MDP semantics, makes it possible to train agents using popular RL algorithms.…”

Section: Deep Statistical Model Checkingmentioning

confidence: 99%

The Modest State of Learning, Sampling, and Verifying Strategies

Hartmanns

Klauck

2022

Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning

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Optimal decision-making under stochastic uncertainty is a core problem tackled in artificial intelligence/machine learning (AI), planning, and verification. Planning and AI methods aim to find good or optimal strategies to maximise rewards or the probability of reaching a goal. Verification approaches focus on calculating the probability or reward, obtaining the strategy as a side effect. In this paper, we connect three strands of work on obtaining strategies implemented in the context of the Modest Toolset: statistical model checking with either lightweight scheduler sampling or deep learning, and probabilistic model checking. We compare their different goals and abilities, and show newly extended experiments on Racetrack benchmarks that highlight the tradeoffs between the methods. We conclude with an outlook on improving the existing approaches and on generalisations to continuous models, and emphasise the need for further tool development to integrate methods that find, evaluate, compare, and explain strategies.

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Section: Deep Statistical Model Checkingmentioning

confidence: 99%

The Modest State of Learning, Sampling, and Verifying Strategies

Hartmanns

Klauck

2022

Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning

Self Cite

View full text Add to dashboard Cite

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MoGym: Using Formal Models for Training and Verifying Decision-making Agents

Gros

Hermanns

Hoffmann

et al. 2022

Computer Aided Verification

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MoGym, is an integrated toolbox enabling the training and verification of machine-learned decision-making agents based on formal models, for the purpose of sound use in the real world. Given a formal representation of a decision-making problem in the JANI format and a reach-avoid objective, MoGym (a) enables training a decision-making agent with respect to that objective directly on the model using reinforcement learning (RL) techniques, and (b) it supports rigorous assessment of the quality of the induced decision-making agent by means of deep statistical model checking (DSMC). MoGym implements the standard interface for training environments established by OpenAI Gym, thereby connecting to the vast body of existing work in the RL community. In return, it makes accessible the large set of existing JANI model checking benchmarks to machine learning research. It thereby contributes an efficient feedback mechanism for improving in particular reinforcement learning algorithms. The connective part is implemented on top of Momba. For the DSMC quality assurance of the learned decision-making agents, a variant of the statistical model checker modes of the Modest Toolset is leveraged, which has been extended by two new resolution strategies for non-determinism when encountered during statistical evaluation.

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Model-Based Diagnosis of Real-Time Systems: Robustness Against Varying Latency, Clock Drift, and Out-of-Order Observations

Köhl

Hermanns

2023

ACM Trans. Embed. Comput. Syst.

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Online fault diagnosis techniques are a key enabler of effective failure mitigation. For real-time systems, the problem of identifying faults is aggravated by timing imprecisions such as varying latency between events and their observation. This paper tackles the challenge of diagnosing faults based on partial observations which are subject to timing imprecisions and potentially made out-of-order due to latency. In this paper, we develop a theory of robust real-time diagnosis importing well-established notions from timed automata theory and the diagnosis of discrete event systems. The theory itself enables a foundational understanding and investigation of the problem and its intricacies. Based on this theory, we further devise an online diagnosis algorithm consuming observations incrementally as they are made and enabling diagnosis, whenever possible, within a bounded worst-case delay. We prove the correctness of the algorithm and its properties with respect to the theory. Aiming at practical feasibility, we also show how to obtain sound but not necessarily complete diagnosis results with space and time requirements bounded by the size of the system model and independent of the number of observations. Finally, using a prototypical implementation, we report on first empirical results obtained by simulation of a small excerpt of an industrial automation example.

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Momba: JANI Meets Python

Cited by 4 publications

References 26 publications

The Modest State of Learning, Sampling, and Verifying Strategies

The Modest State of Learning, Sampling, and Verifying Strategies

MoGym: Using Formal Models for Training and Verifying Decision-making Agents

Model-Based Diagnosis of Real-Time Systems: Robustness Against Varying Latency, Clock Drift, and Out-of-Order Observations

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