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The continuous redesign of processes is crucial for companies in times of tough competition and fast‐changing surrounding conditions. Since the manual redesign of processes is a time‐ and resource‐consuming task, automated redesign will increasingly become a useful alternative. Hence, future redesign projects need to be valuated based on both a manual and an automated redesign approach. The purpose of this paper is to compare the manual and automated process redesign on the basis of the Business Process Management (BPM) lifecycle.

In this paper, the authors compare the manual and automated process redesign on the basis of the Business Process Management (BPM) lifecycle. The results form the basis for a mathematical model that outlines the general economic characteristics of process redesign as well as for the manual and automated approaches. Subsequently, the authors exemplarily apply their model to a set of empirical data with respective assumptions on particular aspects of the automated approach.

In the problem setting described in the paper, the valuation model shows that automated process redesign induces an equal or higher number of optimized processes in a company. Therefore, the authors present a decision support that outlines how much to invest in automated process redesign.

The model considers the cost side of automated process redesign; therefore, further research should be conducted to analyze the possibility of higher returns induced by automated redesign (e.g., through a quicker adaption to real‐world changes). Moreover, for automated redesign, there is no requirement for broad empirical data that should be collected and analyzed as soon as this approach leaves the basic research and prototyping stages.

This paper presents an approach that can be used by companies to estimate the upper limit for investments in manual and automated process redesign. Working under certain general assumptions and independently from actual cost and return values, the paper demonstrates that automated process redesign induces an equal or higher ratio of optimized processes. Thus, companies introducing automated redesign cannot only apply the model to evaluate their investments but can also expect a higher ratio of optimized processes for this approach.

As existing literature primarily focuses on the technical aspects of automated process redesign, these findings contribute to the current body of literature. This paper discusses a first decision‐support for the economic aspects of automated process redesign, particularly with regard to the investments that are required for it. This information is relevant as soon as the approach leaves the stage of a prototype.

In recent years, a great number of top conferences and workshops on artificial intelligence (AI) were held in China, showing Chinese AI plays an important role in the world. Meanwhile, Chinese government announced an ambitious scheme, “New Generation Artificial Intelligence Development Plan,” for the country to become a world leader in AI technologies by 2030. The AI research in China has covered various aspects, ranging from chips to algorithms. This chapter attempts to give an overview of the recent advances of AI research and development in China, as well as some perspectives on the future development of AI in China.

The purpose of this paper is to consider the logical specification, and automated verification, of high‐level robotic behaviours.

The paper uses temporal logic as a formal language for providing abstractions of foraging robot behaviour, and successively extends this to multiple robots, items of food for the robots to collect, and constraints on the real‐time behaviour of robots. For each of these scenarios, proofs of relevant properties are carried out in a fully automated way. In addition to automated deductive proofs in propositional temporal logic, the possibility of having arbitrary numbers of robots involved is considered, thus allowing representations of robot swarms. This leads towards the use of first‐order temporal logics (FOTLs).

The proofs of many properties are achieved using automatic deductive temporal provers for the propositional and FOTLs.

Many details of the problem, such as location of the robots, avoidance, etc. are abstracted away.

Large robot swarms are beyond the current capability of propositional temporal provers. Whilst representing and proving properties of arbitrarily large swarms using FOTLs is feasible, the representation of infinite numbers of pieces of food is outside of the decidable fragment of FOTL targeted, and practically, the provers struggle with even small numbers of pieces of food.

The work described in this paper is novel in that it applies automatic temporal theorem provers to proving properties of robotic behaviour.

A lot of literature is available that discusses personal determinants of organic food consumption. However different models and determinants are used in the literature. This paper aims to provide an overview, within a framework linking Schwartz' values theory and the theory of planned behaviour (TPB). Also it seeks to focus on the importance of affective attitude, emotions, personal norm, involvement and uncertainty related to organic food consumption.

The paper is based on secondary data sources, namely the literature concerning personal determinants of organic food consumption.

Both the values theory and the theory of planned behaviour have been referred to as relevant theories for better understanding consumers' choice for organic food. Organic food consumption decisions can be explained by relating attributes of organic food with more abstract values such as “security”, “hedonism”, “universalism”, “benevolence”, “stimulation”, “self‐direction” and “conformity”. Appealing to these values can positively influence attitudes towards organic food consumption. Besides attitude, subjective and personal norm and (perceived) behavioural control influence consumption of organic food.

More research related to the role of uncertainty (reduction) during the process of buying organic food is recommended.

Relatively little research has examined the affective component of attitude and emotions in relation to organic food consumption, while these may play an important role as drivers of involvement and thus help to jolt food purchasers out of their routine of buying conventional food and set a first step to adopt organic food.

To the authors' knowledge, this is the first paper providing a comprehensive overview and linking the literature on organic food consumption to the values theory and the theory of planned behaviour, including the role of personal norm and focusing on emotions. The proposed integration of mental processing in an organic food consumption model leads to interesting hypotheses and recommendations for policy makers, researchers and stakeholders involved in the organic food market.

Real-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.

The presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.

This work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.

The KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.

The presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.

While automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.

This paper describes an architecture for the usage of Instructional Design (ID) knowledge in intelligent instructional systems. In contrast with other architectures, ontologies are used to represent ID knowledge about both what to teach and how to teach. Moreover, set‐theoretic reasoning is used for the provision of inferential services. In particular, the paper shows how set‐theoretic deductions can be applied (i) to support the modelling of ID knowledge bases, (ii) to retrieve suitable teaching methods from them, and (iii) to detect errors in a training design. The intelligent knowledge management environment CONCEPTOOL is used to demonstrate the benefits of the proposed architecture.

The purpose of the paper is to provide a two‐tier framework for managing semantic‐aware distributed firewall policies to be applied to the devices existing in one administrative domain.

Special attention is paid to the CIM‐based information model defined as the ontology to be used in this framework and the AI‐based reasoning mechanisms and components used to perform the conflict discovery tasks over the distributed firewall policies.

Mechanisms presented allow the solving some of the current issues of the network‐centric security model being used in the Internet. The two‐tier framework designed provides semantic‐aware mechanisms to perform conflict detection and automatic enforcement of policy rules in the distributed firewall scenario. This framework is based on the use of a standard information model and a semantic‐aware policy language to formally define (and then process) firewall policies.

Ongoing work is focused on identifying all kind of conflicts and anomalies that may exist in firewall systems; in parallel to this task a semi‐automatic resolver of conflicting policies is currently under design.

Network and security administrators can specify firewall policies and validate them to find syntactic and semantic errors (i.e. policy conflicts). A framework for automated validation and distribution of policies at different levels is included. This ensures that firewall policies produce the desired effects, facilitating the creation and maintenance of firewall rules in one administrative domain.

A practical and novel two‐tier system that provides detection of conflicts in rules existing in a distributed firewall scenario and the automatic and secure deployment of these rules. A packet‐filtering model, which is simple and powerful enough for the conflict discovery and rule analysis processes, has been proposed. Moreover, ontology and rule reasoning are being proposed as techniques for the conflict detection problem in this particular scenario.

The purpose of this paper is to develop an automated disassembly cell that is flexible and robust to the physical variations of a product. In this way it is capable of dealing with any model of product, regardless of the level of detail in the supplied information.

The concept of cognitive robotics is used to replicate human level expertise in terms of perception and decision making. As a result, difficulties with respect to the uncertainties and variations of the product in the disassembly process are resolved.

Cognitive functions, namely reasoning and execution monitoring, can be used in basic behaviour control to address problems in variations of the disassembly process due to variations in the product's structure particularly across different models of the product.

The paper provides a practical approach to formulating the disassembly domain and behaviour control of the cognitive robotic agent via a high‐level logical programming language that combines domain‐specific heuristic knowledge with search to deal with variations in products and uncertainties that arise during the disassembly process.

Full disassembly automation that is flexible and robust to the uncertainties that may arise potentially replaces human labour in a difficult and hazardous task. Consequently, the disassembly process will be more economically feasible, especially in developed countries.

The paper provides a practical approach to the basic cognitive functions that replicate the human expert's behaviour to the disassembly cell.

Preparing digital mock-ups (DMUs) for finite element analyses (FEAs) is currently a long and tedious task requiring many interactive CAD model transformations. Functional information about components appears to be very useful to speed this preparation process. The purpose of this paper is to shows how DMU components can be automatically enriched with some functional information.

DMUs are widespread and stand as reference model for product description. However, DMUs produced by industrial CAD systems essentially contain geometric models, which lead to tedious preparation of finite element Models (FEMs). Analysis and reasoning approaches are developed to automatically enrich DMUs with functional and kinematic properties. Indeed, geometric interfaces between components form a key starting point to analyze their behaviors under reference states. This is a first stage in a reasoning process to progressively identify mechanical, kinematic as well as functional properties of components.

Inferred semantics adds up to the pure geometric representation provided by a DMU and produce also geometrically structured components. Functional information connected to a structured geometric model of a component significantly improves FEM preparation and increases its robustness because idealizations can take place using components’ functions and components’ structure helps defining sub-domains of FEMs.

Future research will carry on improving algorithms for geometric interfaces identification, processing a wider range of component functions, which will contribute to a formalization of the concept of functional consistency of a DMU.

Simulation engineers benefit from this automated enrichment of DMUs with functional information to speed up the preparation of FEAs of large assemblies.

Research on context‐aware systems design has received significant attention lately. One of the research directions on context‐aware systems is towards context‐aware frameworks adapted to domain‐specific requirements, aiming to improve their applicability in a variety of applications, which share common requirements. The purpose of this paper is to present the design of a domain‐specific context‐aware platform supporting context acquisition, presentation and rule‐based control.

The proposed platform uses a formal context model, based on ontologies description, aiming to provide a common representation of contextual information, facilitating thus integration and reusability in application domains, which embrace a common set of requirements. A context‐aware system has been built upon a well defined data model, which inherits a list of offered functionalities and/or services at the acquisition, presentation and reasoning level. The presented platform entails an event‐driven context‐based inference mechanism aiming to enable automated reasoning.

The proposed platform has been applied in two different case studies, aiming to provide a proof of concept towards the applicability of the proposed framework in different application areas through the offered integration and adaptation mechanisms. Evaluation results, in the frame of these case studies, show the ease of using the platform and its acceptable performance in practice.

As context‐aware applications within specific domains share common requirements, it becomes mandatory to offer solutions, like the proposed in this paper, which can be easily adapted to application semantics and can be reused with different levels of abstractions in order to meet specific requirements. The present paper proposes a domain‐specific platform, which applies on a variety of application types with a common set of requirements.