118,663 research outputs found
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
Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning
Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exacerbated due to the ubiquity and complexity of Artificial Intelligence (AI)-based systems. This is the case of Reinforcement Learning (RL), where autonomous agents learn through trial-and-error how to find good solutions to a problem. Thus, the underlying decision-making criteria may become opaque to users that interact with the system and who may require explanations about the systemâs reasoning. Available work for eXplainable Reinforcement Learning (XRL) offers different trade-offs: e.g. for runtime explanations, the approaches are model-specific or can only analyse results after-the-fact. Different from these approaches, this paper aims to provide an online model-agnostic approach for XRL towards trustworthy and understandable AI. We present ETeMoX, an architecture based on temporal models to keep track of the decision-making processes of RL systems. In cases where the resources are limited (e.g. storage capacity or time to response), the architecture also integrates complex event processing, an event-driven approach, for detecting matches to event patterns that need to be stored, instead of keeping the entire history. The approach is applied to a mobile communications case study that uses RL for its decision-making. In order to test the generalisability of our approach, three variants of the underlying RL algorithms are used: Q-Learning, SARSA and DQN. The encouraging results show that using the proposed configurable architecture, RL developers are able to obtain explanations about the evolution of a metric, relationships between metrics, and were able to track situations of interest happening over time windows
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Intelligent monitoring of business processes using case-based reasoning
The work in this thesis presents an approach towards the effective monitoring of business processes using Case-Based Reasoning (CBR). The rationale behind this research was that business processes constitute a fundamental concept of the modern world and there is a constantly emerging need for their efficient control. They can be efficiently represented but not necessarily monitored and diagnosed effectively via an appropriate platform.
Motivated by the above observation this research pursued to which extent there can be efficient monitoring, diagnosis and explanation of the workflows. Workflows and their effective representation in terms of CBR were investigated as well as how similarity measures among them could be established appropriately. The monitoring results and their following explanation to users were questioned as well as which should be an appropriate software architecture to allow monitoring of workflow executions.
Throughout the progress of this research, several sets of experiments have been conducted using existing enterprise systems which are coordinated via a predefined workflow business process. Past data produced over several years have been used for the needs of the conducted experiments. Based on those the necessary knowledge repositories were built and used afterwards in order to evaluate the suggesting approach towards the effective monitoring and diagnosis of business processes.
The produced results show to which extent a business process can be monitored and diagnosed effectively. The results also provide hints on possible changes that would maximize the accuracy of the actual monitoring, diagnosis and explanation. Moreover the presented approach can be generalised and expanded further to enterprise systems that have as common characteristics a possible workflow representation and the presence of uncertainty.
Further work motivated by this thesis could investigate how the knowledge acquisition can be transferred over workflow systems and be of benefit to large-scale multidimensional enterprises. Additionally the temporal uncertainty could be investigated further, in an attempt to address it while reasoning. Finally the provenance of cases and their solutions could be explored further, identifying correlations with the process of reasoning
Towards Activity Context using Software Sensors
Service-Oriented Computing delivers the promise of configuring and
reconfiguring software systems to address user's needs in a dynamic way.
Context-aware computing promises to capture the user's needs and hence the
requirements they have on systems. The marriage of both can deliver ad-hoc
software solutions relevant to the user in the most current fashion. However,
here it is a key to gather information on the users' activity (that is what
they are doing). Traditionally any context sensing was conducted with hardware
sensors. However, software can also play the same role and in some situations
will be more useful to sense the activity of the user. Furthermore they can
make use of the fact that Service-oriented systems exchange information through
standard protocols. In this paper we discuss our proposed approach to sense the
activity of the user making use of software
A conceptual architecture for semantic web services development and deployment
Several extensions of the Web Services Framework (WSF) have been proposed. The combination with Semantic Web technologies introduces a notion of semantics, which can enhance scalability through automation. Service composition to processes is an equally important issue. Ontology technology â the core of the Semantic Web â can be the central building block of an extension endeavour. We present a conceptual architecture for ontology-based Web service development and deployment. The development of service-based software systems within the WSF is gaining increasing importance. We show how ontologies can integrate models, languages, infrastructure, and activities within this architecture to support reuse and composition of semantic Web services
Models of everywhere revisited: a technological perspective
The concept âmodels of everywhereâ was first introduced in the mid 2000s as a means of reasoning about the
environmental science of a place, changing the nature of the underlying modelling process, from one in which
general model structures are used to one in which modelling becomes a learning process about specific places, in
particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another
it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere,
models of everything and models at all times, being constantly re-evaluated against the most current
evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities.
However, the approach has, as yet, not been fully utilised or explored. This paper examines the
concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first
proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud
computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again
at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the
remaining research questions. The paper concludes by identifying the key elements of a research agenda that
should underpin such experimentation and deployment
Increasing information feed in the process of structural steel design
Research initiatives throughout history have shown how a designer typically makes associations and references to a vast amount of knowledge based on experiences to make decisions. With the increasing usage of information systems in our everyday lives, one might imagine an information system that provides designers access to the âarchitectural memoriesâ of other architectural designers during the design process, in addition to their own physical architectural memory. In this paper, we discuss how the increased adoption of semantic web technologies might advance this idea. We investigate to what extent information can be described with these technologies in the context of structural steel design. This investigation indicates significant possibilities regarding information reuse in the process of structural steel design and, by extent, in other design contexts as well. However, important obstacles and question remarks can still be outlined as well
Early aspects: aspect-oriented requirements engineering and architecture design
This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications
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