Maximum Causal Entropy Specification Inference from Demonstrations

In many settings (e.g., robotics) demonstrations provide a natural way to specify tasks; however, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the tasks, such as rewards or policies, can be safely composed and/or do not explicitly capture history dependencies. Motivated by this deficit, recent works have proposed learning Boolean task specifications, a class of Boolean non-Markovian rewards which admit well-defined composition and explicitly handle historical dependencies. This work continues this line of research by adapting maximum causal entropy inverse reinforcement learning to estimate the posteriori probability of a specification given a multi-set of demonstrations. The key algorithmic insight is to leverage the extensive literature and tooling on reduced ordered binary decision diagrams to efficiently encode a time unrolled Markov Decision Process. This enables transforming a naive exponential time algorithm into a polynomial time algorithm.Comment: Computer Aided Verification, 202

Net (in finite geometry)

Quiescence is a fundamental concept in modelling system behaviour, as it explicitly represents the fact that no output is produced in certain states. The notion of quiescence is also essential to model-based testing: if a particular implementation under test does not provide any output, then the test evaluation algorithm must decide whether or not to allow this behaviour. To explicitly model quiescence in all its glory, we introduce Divergent Quiescent Transition Systems (DQTSs). DQTSs model quiescence using explicit delta-labelled transitions, analogous to Suspension Automata (SAs) in the well-known ioco framework. Whereas SAs have only been defined implicitly, DQTSs for the first time provide a fully-formalised framework for quiescence. Also, while SAs are restricted to convergent systems (i.e., without tau-cycles), we show how quiescence can be treated naturally using a notion of fairness, allowing systems exhibiting divergence to be modelled as well. We study compositionality under the familiar automata-theoretical operations of determinisation, parallel composition and action hiding. We provide a non-trivial algorithm for detecting divergent states, and discuss its complexity. Finally, we show how to use DQTSs in the context of model-based testing, for the first time presenting a full-fledged theory that allows ioco to be applied to divergent systems

Relating Paths in Transition Systems: The Fall of the Modal Mu-Calculus

International audienceno abstrac

Analyzing “The Kid” Movie in Terms of Adlerian Theory

Refinement is a key concept in the B-Method. While refinement is at the heart of the B Method, so far no automatic refinement checker has been developed for it. In this paper we present a refinement checking algorithm and implementation for B. It is based on using an operational semantics of B, obtained in practice by the ProB animator. The refinement checker has been integrated into ProB toolset and we present various case studies and empirical results in the paper, showing the algorithm to be surprisingly effective. The algorithm checks that a refinement preserves the trace properties of a specification. We also compare our tool against the refinement checker FDR for CSP and discuss an extension for singleton failure refinement