Do I need to fix a failed component now, or can I wait until tomorrow?

We investigate how predictive event-based modelling can inform operational decision making in complex systems with component failures. By relating the status of components to service availability, and using stochastic temporal logic reasoning, we quantify the risk of service failure now, and in the future, after a given elapsed time. Decisions can then be taken according to those risks. We demonstrate the approach through application to an industrial case study system in which component failures are sensed and monitored. The system has been deployed for some time. A novel aspect is we calibrate the model(s) according to inferences over historical field data, thus the results of our reasoning can inform decision making in the actual deployed system

Assume-guarantee verification for probabilistic systems

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

Designing Computationally Creative Musical Performance Systems

This is work in progress where we outline a design process for a computationally creative musical performance system using the Creative Systems Framework (CSF). The proposed system is intended to produce virtuosic interpretations, and subsequent synthesized renderings of these interpretations with a physical model of a bass guitar, using case-based reasoning and reflection. We introduce our interpretations of virtuosity and musical performance, outline the suitability of case-based reasoning in computationally creative systems and introduce notions of computational creativity and the CSF. We design our system by formalising the components of the CSF and briefly outline a potential implementation. In doing so, we demonstrate how the CSF can be used as a tool to aid in designing computationally creative musical performance systems

Efficient Aggregated Deliveries with Strong Guarantees in an Event-based Distributed System

A popular approach to designing large scale distributed systems is to follow an event-based approach. In an event-based approach, a set of software components interact by producing and consuming events. The event-based model allows for the decoupling of software components, allowing distributed systems to scale to a large number of components. Event correlation allows for higher order reasoning of events by constructing complex events from single, consumable events. In many cases, event correlation applications rely on centralized setups or broker overlay networks. In the case of centralized setups, the guarantees for complex event delivery are stronger, however, centralized setups create performance bottlenecks and single points of failure. With broker overlays, the performance and fault tolerance are improved but at the cost of weaker guarantees

A Causality Analysis Framework for Component-Based Real-Time Systems

We propose an approach to enhance the fault diagnosis in black-box component-based systems, in which only events on component interfaces are observable, and assume that causal dependencies between component interface events within components are not known. For such systems, we describe a causality analysis framework that helps us establish the causal relationship between component failures and system failures, given an observed system execution trace. The analysis is based on a formalization of counterfactual reasoning, and applicable to real-time systems. We illustrate the analysis with a case study from the medical device domain

Controlling Concurrent Change - A Multiview Approach Toward Updatable Vehicle Automation Systems

The development of SAE Level 3+ vehicles [{SAE}, 2014] poses new challenges not only for the functional development, but also for design and development processes. Such systems consist of a growing number of interconnected functional, as well as hardware and software components, making safety design increasingly difficult. In order to cope with emergent behavior at the vehicle level, thorough systems engineering becomes a key requirement, which enables traceability between different design viewpoints. Ensuring traceability is a key factor towards an efficient validation and verification of such systems. Formal models can in turn assist in keeping track of how the different viewpoints relate to each other and how the interplay of components affects the overall system behavior. Based on experience from the project Controlling Concurrent Change, this paper presents an approach towards model-based integration and verification of a cause effect chain for a component-based vehicle automation system. It reasons on a cross-layer model of the resulting system, which covers necessary aspects of a design in individual architectural views, e.g. safety and timing. In the synthesis stage of integration, our approach is capable of inserting enforcement mechanisms into the design to ensure adherence to the model. We present a use case description for an environment perception system, starting with a functional architecture, which is the basis for componentization of the cause effect chain. By tying the vehicle architecture to the cross-layer integration model, we are able to map the reasoning done during verification to vehicle behavior

A Classification Approach for Automated Reasoning Systems--A Case Study in Graph Theory

Reasoning systems which create classifications of structured objects face the problem of how object descriptions can be used to reflect their components as well as relations among these components. Current reasoning systems on graph theory do not adequately provide models to discover complex relations among mathematical concepts (eg: relations involving subgraphs) mainly due to the inability to solve this problem. This thesis presents an approach to construct a knowledge-based system, GC (Graph Classification), which overcomes this difficulty in performing automated reasoning in graph theory. We describe graph concepts based on an attribute called Linear Recursive Constructivity (LRC). LRC defines classes by an algebraic formula supported by background knowledge of graph types. We use subsumption checking on decomposed algebraic expressions of graph classes as a major proof method. The search is guided by case-split-based inferencing. Using the approach GC has generated proofs for many theorems such as any two distinct cycles (closed paths) having a common edge e contain a cycle not traversing e , if cycle C1 contains edges e1, e2, and cycle C2 contains edges e2, e3, then there exists a cycle that contains e1 and e3 and the union of a tree and a path is a tree if they have only a single common vertex. The main contributions of this thesis are: (1) Development of a classification-based knowledge representation and a reasoning approach for graph concepts, thus providing a simple model for structured mathematical objects. (2) Development of an algebraic theory for simplifying and decomposing graph concepts. (3) Development of a proof search and a case-splitting technique with the guidance of graph type knowledge. (4) Development of a proving mechanism that can be generate constructive proofs by manipulating only simple linear formalization of theorems

Topological augmentation: A step forward for qualitative partition reasoning

The current state of the art for partition based qualitative spatial reasoning systems such as the 9-intersection, 9+-intersection, direction relation matrix, and peripheral direction relations is that of the binary set intersection — either empty or non-empty — conveying the intersection (or lack thereof) of an object in the sets deriving the partition. While such representations are sufficient for topological components of objects, these representations are not sufficient for various tasks in qualitative spatial reasoning (composition, representation transfer, converse, etc.) regarding partitions as tiles. Topological augmentation expands the current binary status quo into a system of assigning topological relations between objects and tiles. A case study is presented in the form of the direction relation matrix, demonstrating that an increased vocabulary has benefits for spatial information systems, providing localized context within a qualitative embedding

Autonomous CPSoS for cognitive large manufacturing industries.

The general aim of a cognitive Cyber Physical System of Systems (CPSoS) is to provide managed access to data in a smart fashion such that sensing and actuation capabilities are connected. Whilst there is significant funding and research devoted to this area, focus remains purely on creating bespoke systems. This paper presents a novel approach, based on a set of components to leverage Situational Awareness and Smart Actuation in large manufacturing industries with the focus on enabling predictive maintenance for asset and abnormal situation management. This paper presents a novel generic platform, named AtiCoS, that combines case-based and common-sense reasoning, as the enabling methodologies for enhancing CPSoS with cognitive capabilities