MCMAS: an open-source model checker for the verification of multi-agent systems

We present MCMAS, a model checker for the verification of multi-agent systems. MCMAS supports efficient symbolic techniques for the verification of multi-agent systems against specifications representing temporal, epistemic and strategic properties. We present the underlying semantics of the specification language supported and the algorithms implemented in MCMAS, including its fairness and counterexample generation features. We provide a detailed description of the implementation. We illustrate its use by discussing a number of examples and evaluate its performance by comparing it against other model checkers for multi-agent systems on a common case study

Strategic logics : complexity, completeness and expressivity

by transferring normative attributes from an agent to another. Such interactions are called delegation. Formal models of delegation and control were studied in, e.g., [189, 149, 191]. In this work, we consider the scenario where agents delegate control over propositions to other agents. The distinction between controllable and uncontrollable propositions stems from areas like discrete event systems and control theory, where, e.g., Boutilier [39] studied control in the context of deontic logic. Control and controllable propositions were also studied in [52, 66, 249, 248]. We now give an overview of the thesis. The main purpose of Chapter 2 is to introduce basic concepts and notation and to review relevant literature. The first section presents a brief survey on modal logic. Then, in sections 2.2, 2.3 and 2.4, we introduce epistemic, temporal and strategic modal logics and state known results that characterise their expressivity and computational complexity. In particular, we consider variants of ATL as extensions of branching-time logics. With such ATL-like logics we can describe dynamic multi-agent interactions. In Section 2.5, we discuss extensions of ATL with epistemic notions. Additionally, we suggest a framework for memory-bounded strategic reasoning. In particular, we introduce an epistemic variant of ATL that accounts for agents with limited memory resources as this case was neglected in the literature to date. In Chapter 3, we investigate the computational complexity of ATL and its epistemic extension ATEL. We show in detail how 'the complexity of the satisfiability problem for both logics can be settled at ExpTIME-complete. The part of the chapter about ATL is based on the paper 'ATL Satisfiability is Indeed ExpTIME-COmplete' by Walther, Lutz, Wolter and Wooldridge in the Journal of Logic and Computation, 2006 (265)' and the part about ATEL is based on the paper 'ATEL with Common and Distributed Knowledge is ExpTime-Complete' by Walther which was presented at the 4th Workshop on Methods for Modalities, Humbolt University, Berlin, December 1-2, 2005 [264]. In Chapter 4, we aim to extend the expressiveness of ATL without increasing its computational complexity. We introduce explicit names for strategies in the object language and extend modal operators with the possibility to bind agents to strategy names. In this way, we can fix the decisions of agents that possibly belong to several coalitions. By identifying the behaviqur of agents, we can reason about the effects of agents changing coalitions. Dynamic coalitions provide more flexibility to adapt abilities to a changing environment. We investigate the expressivity of the resulting logic ATLES and compare it to ATL and ATL*. Moreover, we formulate two model checking problems for ATLES and investigate their complexity as well as the complexity of the satisfiability problem for ATLES. Additionally, we present a complete axiomatisation. This chapter is based on the paper 'Alternating-time Temporal Logic with Explicit Strategies' by Walther, van der Hoek and Wooldridge which is going to presented at the 11th Conference on Theoretical Aspects of Rationality and Knowledge (TARK), Brussels, Belgium, June 25-27, 2007 [266]

Logical and deep learning methods for temporal reasoning

In this thesis, we study logical and deep learning methods for the temporal reasoning of reactive systems. In Part I, we determine decidability borders for the satisfiability and realizability problem of temporal hyperproperties. Temporal hyperproperties relate multiple computation traces to each other and are expressed in a temporal hyperlogic. In particular, we identify decidable fragments of the highly expressive hyperlogics HyperQPTL and HyperCTL*. As an application, we elaborate on an enforcement mechanism for temporal hyperproperties. We study explicit enforcement algorithms for specifications given as formulas in universally quantified HyperLTL. In Part II, we train a (deep) neural network on the trace generation and realizability problem of linear-time temporal logic (LTL). We consider a method to generate large amounts of additional training data from practical specification patterns. The training data is generated with classical solvers, which provide one of many possible solutions to each formula. We demonstrate that it is sufficient to train on those particular solutions such that the neural network generalizes to the semantics of the logic. The neural network can predict solutions even for formulas from benchmarks from the literature on which the classical solver timed out. Additionally, we show that it solves a significant portion of problems from the annual synthesis competition (SYNTCOMP) and even out-of-distribution examples from a recent case study.Diese Arbeit befasst sich mit logischen Methoden und mehrschichtigen Lernmethoden für das zeitabhängige Argumentieren über reaktive Systeme. In Teil I werden die Grenzen der Entscheidbarkeit des Erfüllbarkeits- und des Realisierbarkeitsproblem von temporalen Hypereigenschaften bestimmt. Temporale Hypereigenschaften setzen mehrere Berechnungsspuren zueinander in Beziehung und werden in einer temporalen Hyperlogik ausgedrückt. Insbesondere werden entscheidbare Fragmente der hochexpressiven Hyperlogiken HyperQPTL und HyperCTL* identifiziert. Als Anwendung wird ein Enforcement-Mechanismus für temporale Hypereigenschaften erarbeitet. Explizite Enforcement-Algorithmen für Spezifikationen, die als Formeln in universell quantifiziertem HyperLTL angegeben werden, werden untersucht. In Teil II wird ein (mehrschichtiges) neuronales Netz auf den Problemen der Spurgenerierung und Realisierbarkeit von Linear-zeit Temporallogik (LTL) trainiert. Es wird eine Methode betrachtet, um aus praktischen Spezifikationsmustern große Mengen zusätzlicher Trainingsdaten zu generieren. Die Trainingsdaten werden mit klassischen Solvern generiert, die zu jeder Formel nur eine von vielen möglichen Lösungen liefern. Es wird gezeigt, dass es ausreichend ist, an diesen speziellen Lösungen zu trainieren, sodass das neuronale Netz zur Semantik der Logik generalisiert. Das neuronale Netz kann Lösungen sogar für Formeln aus Benchmarks aus der Literatur vorhersagen, bei denen der klassische Solver eine Zeitüberschreitung hatte. Zusätzlich wird gezeigt, dass das neuronale Netz einen erheblichen Teil der Probleme aus dem jährlichen Synthesewettbewerb (SYNTCOMP) und sogar Beispiele außerhalb der Distribution aus einer aktuellen Fallstudie lösen kann

New Directions in Model Checking Dynamic Epistemic Logic

Dynamic Epistemic Logic (DEL) can model complex information scenarios in a way that appeals to logicians. However, its existing implementations are based on explicit model checking which can only deal with small models, so we do not know how DEL performs for larger and real-world problems. For temporal logics, in contrast, symbolic model checking has been developed and successfully applied, for example in protocol and hardware verification. Symbolic model checkers for temporal logics are very efficient and can deal with very large models. In this thesis we build a bridge: new faithful representations of DEL models as so-called knowledge and belief structures that allow for symbolic model checking. For complex epistemic and factual change we introduce transformers, a symbolic replacement for action models. Besides a detailed explanation of the theory, we present SMCDEL: a Haskell implementation of symbolic model checking for DEL using Binary Decision Diagrams. Our new methods can solve well-known benchmark problems in epistemic scenarios much faster than existing methods for DEL. We also compare its performance to to existing model checkers for temporal logics and show that DEL can compete with established frameworks. We zoom in on two specific variants of DEL for concrete applications. First, we introduce Public Inspection Logic, a new framework for the knowledge of variables and its dynamics. Second, we study the dynamic gossip problem and how it can be analyzed with epistemic logic. We show that existing gossip protocols can be improved, but that no perfect strengthening of "Learn New Secrets" exists