Counterfactual Causality from First Principles?

In this position paper we discuss three main shortcomings of existing approaches to counterfactual causality from the computer science perspective, and sketch lines of work to try and overcome these issues: (1) causality definitions should be driven by a set of precisely specified requirements rather than specific examples; (2) causality frameworks should support system dynamics; (3) causality analysis should have a well-understood behavior in presence of abstraction.Comment: In Proceedings CREST 2017, arXiv:1710.0277

Trend-based analysis of a population model of the AKAP scaffold protein

We formalise a continuous-time Markov chain with multi-dimensional discrete state space model of the AKAP scaffold protein as a crosstalk mediator between two biochemical signalling pathways. The analysis by temporal properties of the AKAP model requires reasoning about whether the counts of individuals of the same type (species) are increasing or decreasing. For this purpose we propose the concept of stochastic trends based on formulating the probabilities of transitions that increase (resp. decrease) the counts of individuals of the same type, and express these probabilities as formulae such that the state space of the model is not altered. We define a number of stochastic trend formulae (e.g. weakly increasing, strictly increasing, weakly decreasing, etc.) and use them to extend the set of state formulae of Continuous Stochastic Logic. We show how stochastic trends can be implemented in a guarded-command style specification language for transition systems. We illustrate the application of stochastic trends with numerous small examples and then we analyse the AKAP model in order to characterise and show causality and pulsating behaviours in this biochemical system

The xSAP Safety Analysis Platform

This paper describes the xSAP safety analysis platform. xSAP provides several model-based safety analysis features for finite- and infinite-state synchronous transition systems. In particular, it supports library-based definition of fault modes, an automatic model extension facility, generation of safety analysis artifacts such as Dynamic Fault Trees (DFTs) and Failure Mode and Effects Analysis (FMEA) tables. Moreover, it supports probabilistic evaluation of Fault Trees, failure propagation analysis using Timed Failure Propagation Graphs (TFPGs), and Common Cause Analysis (CCA). xSAP has been used in several industrial projects as verification back-end, and is currently being evaluated in a joint R&D Project involving FBK and The Boeing Company

On interoperability and conformance assessment in service composition

The process of composing a service from other services typically involves multiple models. These models may represent the service from distinct perspectives, e.g., to model the different roles of systems involved in the service, and at distinct abstraction levels, e.g., to model the service’s capability, interface or the orchestration that implements the service. The consistency among these models needs to be maintained in order to guarantee the correctness of the composition process. Two types of consistency relations are distinguished: interoperability, which concerns the ability of different roles to interoperate, and conformance, which concerns the correct implementation of an abstract model by a more concrete model. This paper discusses the need for and use of techniques to assess interoperability and conformance in a service composition process. The paper shows how these consistency relations can be described and analysed using concepts from the COSMO framework. Examples are presented to illustrate how interoperability and conformance can be assessed