The COMICS Tool – Computing Minimal Counterexamples for DTMCs

Jansen, Nils; Ábrahám, Erika; Volk, Matthias; Wimmer, Ralf; Katoen, Joost-Pieter; Becker, Bernd

doi:10.1007/978-3-642-33386-6_27

Cited by 20 publications

(16 citation statements)

References 16 publications

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“…In this section we evaluate our MILP formulations and heuristics on a number of DTMC and MDP benchmarks from the Prism benchmark-suite [57,58]. We compare our results with the tool Comics [49], which implements heuristic approaches to compute small subsystems for DTMCs. It has two modes: the local search extends a given subsystem by short paths that carry much probability, whereas the global search searches for the next most probable path from the initial state to goal, and adds it to the subsystem.…”

Section: Methodsmentioning

confidence: 99%

“…Model checking MDPs in the presence of multiple objectives has been studied in [36,38]. Heuristic approaches for computing small witnessing subsystems in DTMCs have been proposed in [5,7,48,50,51] and implemented in the tool Comics [49]. Witnessing subsystems in MDPs have been considered in [6,9] and [19], which focuses on succinctly representing witnessing schedulers.…”

Section: Property Certificate Dimension Certificate Conditionmentioning

confidence: 99%

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Farkas Certificates and Minimal Witnesses for Probabilistic Reachability Constraints

Funke

Jantsch

Baier

2020

Lecture Notes in Computer Science

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This paper introduces Farkas certificates for lower and upper bounds on minimal and maximal reachability probabilities in Markov decision processes (MDP), which we derive using an MDP-variant of Farkas' Lemma. The set of all such certificates is shown to form a polytope whose points correspond to witnessing subsystems of the model and the property. This allows translating the problem of finding minimal witnesses to the problem of finding vertices with a maximal number of zeros. While computing such vertices is computationally hard in general, we derive new heuristics from our formulations that exhibit competitive performance compared to state-of-the-art techniques and apply to more situations. As an argument that asymptotically better algorithms cannot be hoped for, we show that the decision version of finding minimal witnesses is NP-complete even for acyclic Markov chains.

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Section: Methodsmentioning

confidence: 99%

Section: Property Certificate Dimension Certificate Conditionmentioning

confidence: 99%

Farkas Certificates and Minimal Witnesses for Probabilistic Reachability Constraints

Funke

Jantsch

Baier

2020

Lecture Notes in Computer Science

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“…In essence, we take a weighted-sum approach for solving the multi-objective optimization problem (9). Assuming that Π S = {π 1 , π 2 , π 3 , .…”

Section: Counterexample-guided Apprenticeship Learningmentioning

confidence: 99%

“…If it does, then it will be added to Π S . Otherwise, a counterexample generator, such as COMICS [9], is used to generate a (minimal) counterexample cex πω , which will be added to CEX. σ, α ∈ (0, 1) ← Error bound σ and step length α for the parameter k; 6: Initialization: 7:…”

Section: Counterexample-guided Apprenticeship Learningmentioning

confidence: 99%

Safety-Aware Apprenticeship Learning

Zhou

2018

Computer Aided Verification

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Abstract. Apprenticeship learning (AL) is a kind of Learning fromDemonstration techniques where the reward function of a Markov Decision Process (MDP) is unknown to the learning agent and the agent has to derive a good policy by observing an expert's demonstrations. In this paper, we study the problem of how to make AL algorithms inherently safe while still meeting its learning objective. We consider a setting where the unknown reward function is assumed to be a linear combination of a set of state features, and the safety property is specified in Probabilistic Computation Tree Logic (PCTL). By embedding probabilistic model checking inside AL, we propose a novel counterexample-guided approach that can ensure safety while retaining performance of the learnt policy. We demonstrate the effectiveness of our approach on several challenging AL scenarios where safety is essential.

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“…We also changed the counterexample selection scheme to potentially lower the complexity. Furthermore, we developed the supervisor synthesis software in C++ using counterexample generation tool COMICS [24] and L* learning library libalf [25]. This paper is divided into five parts.…”

Section: Introductionmentioning

confidence: 99%

Permissive Supervisor Synthesis for Markov Decision Processes Through Learning

Zhang

Lin

2019

IEEE Trans. Automat. Contr.

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This paper considers the permissive supervisor synthesis for probabilistic systems modeled as Markov Decision Processes (MDP). Such systems are prevalent in power grids, transportation networks, communication networks and robotics. Unlike centralized planning and optimization based planning, we propose a novel supervisor synthesis framework based on learning and compositional model checking to generate permissive local supervisors in a distributed manner. With the recent advance in assume-guarantee reasoning verification for probabilistic systems, building the composed system can be avoided to alleviate the state space explosion and our framework learn the supervisors iteratively based on the counterexamples from verification. Our approach is guaranteed to terminate in finite steps and to be correct.

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The COMICS Tool – Computing Minimal Counterexamples for DTMCs

Cited by 20 publications

References 16 publications

Farkas Certificates and Minimal Witnesses for Probabilistic Reachability Constraints

Farkas Certificates and Minimal Witnesses for Probabilistic Reachability Constraints

Safety-Aware Apprenticeship Learning

Permissive Supervisor Synthesis for Markov Decision Processes Through Learning

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