Regular Model Checking Approach to Knowledge Reasoning over Parameterized Systems (technical report)
Daniel Stan, Anthony Widjaja Lin

TL;DR
This paper introduces a regular model checking framework for verifying epistemic properties in parameterized multi-agent systems, using automata and learning algorithms to handle undecidability issues.
Contribution
It extends public announcement logic with iterated operators and proposes a method to decide verification problems via automata learning techniques.
Findings
Decidability achieved with a disappearance relation.
Automata learning applied to epistemic verification.
Successful case studies on classic puzzles.
Abstract
We present a general framework for modelling and verifying epistemic properties over parameterized multi-agent systems that communicate by truthful public announcements. In our framework, the number of agents or the amount of certain resources are parameterized (i.e. not known a priori), and the corresponding verification problem asks whether a given epistemic property is true regardless of the instantiation of the parameters. For example, in a muddy children puzzle, one could ask whether each child will eventually find out whether (s)he is muddy, regardless of the number of children. Our framework is regular model checking (RMC)-based, wherein synchronous finite-state automata (equivalently, monadic second-order logic over words) are used to specify the systems. We propose an extension of public announcement logic as specification language. Of special interests is the addition of the…
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Taxonomy
TopicsMachine Learning and Algorithms · Formal Methods in Verification · Logic, Reasoning, and Knowledge
