While temporal logic in its various forms has proven essential to reason about reactive systems, agent-based scenarios are typically specified by considering high-level agents attitudes. In particular, specification languages based on epistemic logic [7], or logics for knowledge, have proven useful in a variety of areas including robotics, security protocols, web-services, etc. For example, security specifications involving anonymity [4] are known to be naturally expressible in epistemic formalisms as they explicitly state the lack of different kinds of knowledge of the principals.More generally, various extensions of temporal logic have been studied in agents and AI contexts to represent properties of autonomous systems. In addition to epistemic operators, at the very core of these approaches is the importance of deontic modalities expressing norms and compliance/violation with respect to previously agreed commitments, and ATL-like modalities expressing cooperation among agents.While these languages have been long explored and appropriate semantics developed, until recently there has been a remarkable gap in the availability of efficient symbolic model checking toolkits supporting these. In this paper we describe MCMAS, a symbolic model checker specifically tailored to agent-based specifications and scenarios. MCMAS [12] supports specifications based on CTL, epistemic logic (including operators of common and distributed knowledge) [7], Alternating Time Logic [2], and deontic modalities for correctness [16]. The release described in this abstract is a complete rebuild of a preliminary experimental checker [14]. The model input language includes variables and basic types and it implements the semantics of interpreted systems, thereby naturally supporting the modularity present in agent-based systems. MCMAS implements OBDD-based algorithms optimised for interpreted systems and supports fairness, counter-example generation, and interactive execution (both in explicit and symbolic mode). MCMAS has been used in a variety of scenarios including web-services, diagnosis, and security. MCMAS is released under GNU-GPL. Multi-Agent Systems FormalismsMulti-Agent Systems (MAS) formalisms are typically built on extensions of computational tree logic (CTL). For the purposes of this abstract we consider specifications given in the following language L built from a set of propositional atoms p ∈ P , and a set of agents i ∈ A (G ⊆ A denotes a set of agents):⋆

Abstract. We present a verification framework for analysing multiple quantitative objectives of systems that exhibit both nondeterministic and stochastic behaviour. These systems are modelled as probabilistic automata, enriched with cost or reward structures that capture, for example, energy usage or performance metrics. Quantitative properties of these models are expressed in a specification language that incorporates probabilistic safety and liveness properties, expected total cost or reward, and supports multiple objectives of these types. We propose and implement an efficient verification framework for such properties and then present two distinct applications of it: firstly, controller synthesis subject to multiple quantitative objectives; and, secondly, quantitative compositional verification. The practical applicability of both approaches is illustrated with experimental results from several large case studies.

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.

Abstract. We present a compositional verification technique for systems that exhibit both probabilistic and nondeterministic behaviour. We adopt an assume-guarantee approach to verification, where both the assumptions made about system components and the guarantees that they provide are regular safety properties, represented by finite automata. Unlike previous proposals for assume-guarantee reasoning about probabilistic systems, our approach does not require that components interact in a fully synchronous fashion. In addition, the compositional verification method is efficient and fully automated, based on a reduction to the problem of multi-objective probabilistic model checking. We present asymmetric and circular assume-guarantee rules, and show how they can be adapted to form quantitative queries, yielding lower and upper bounds on the actual probabilities that a property is satisfied. Our techniques have been implemented and applied to several large case studies, including instances where conventional probabilistic verification is infeasible.

Abstract-Quantitative verification techniques provide an effective means of computing performance and reliability properties for a wide range of systems. However, the computation required can be expensive, particularly if it has to be performed multiple times, for example to determine optimal system parameters. We present efficient incremental techniques for quantitative verification of Markov decision processes, which are able to re-use results from previous verification runs, based on a decomposition of the model into its strongly connected components (SCCs). We also show how this SCC-based approach can be further optimised to improve verification speed and how it can be combined with symbolic data structures to offer better scalability. We illustrate the effectiveness of the approach on a selection of large case studies.