A three-level knowledge-based system for the generation of live and safe Petri nets for manufacturing systems

New generation manufacturing systems are involved in a transformation process which aims for more reliable production processes and with a lower response time to the demand of the market. This work presents an application of artificial intelligence planning techniques for the automatic generation of the control program for a manufacturing system expressed as a safe and live Petri net. The advantage of the system presented here is straightforward: it allows for a fast generation of sound results free of human errors, reducing the cost and duration of the development phase of control programs.     

Planning of collision-free paths for a reconfigurable dual manipulator equipped mobile robot

In this paper, we study the problem of finding a collision-free path for a mobile robot which possesses manipulators. The task of the robot is to carry a polygonal object from a starting point to a destination point in a possibly culttered environment. In most of the existing research on robot path planning, a mobile robot is approximated by a fixed shape, i.e., a circle or a polygon. In our task planner, the robot is allowed to change configurations for avoiding collision. This path planner operates using two algorithms: the collision-free feasible configuration finding algorithm and the collision-free path finding algorithm. The collision-free feasible configuration finding algorithm finds all collision-free feasible configurations for the robot when the position of the carried object is given. The collision-free path finding algorithm generates some candidate paths first and then uses a graph search method to find a collision-free path from all the collision-free feasible configurations along the candidate paths. The proposed algorithms can deal with a cluttered environment and is guaranteed to find a solution if one exists.     

Machine planning for manufacturing: dynamic resource allocation and on-line supervisory control

In this paper*, we provide tools for integrating machine planning and manufacturing. Specifically, we show how assembly trees can be coded into operators for machine planners and how machine planners can represent flow-lines, assembly and job-shop choices. We provide a polynomial-time algorithm for succinctly combining multiple plans; the resulting plan can be expressed as four matrices that are equivalent to a Petri net. We also provide a dynamic supervisory controller that can execute a single plan or switch between multiple plans as real-time conditions change.     

Evaluating background and prior knowledge: A case study on engineering graphics learning

In this work, we analyze the results obtained by a group of freshmen students in a test of prior knowledge on technical drawing carried out before and after attending an introductory course on this subject. The aim of this course was to help students to gain knowledge necessary to take full advantage of the CAD software that is regularly used to teach engineering graphics education in the classroom. Two aspects of the results will be analyzed. First, if this kind of course is good enough to show an improvement in students’ ability. As will be shown, the score obtained by the students improved after attending the course. Second, we have analyzed the results in order to know if the students’ prior knowledge is good enough to allow them coping with their first year. Although the answer to this question depends on many factors, it will be shown that an introductory course is a useful tool to reduce the possible gap between the students’ prior knowledge and the basic knowledge on geometry that should be requested to a first-year engineering student. The kind of test we propose here could also be implemented to test the cognitive abilities of the students.     

The usefulness of mobile multimedia communications: a case study

To design new technologies, such as mobile multimedia communications, in a proper way, early assessment of their usefulness and requirements is needed. However, early assessment still lacks a sound methodology. This article presents an early assessment method that has been developed in a case study on the usefulness of mobile multimedia communications. After the introduction, the method will be briefly explained. The method consists of the following stages: preliminary investigation, analysis, scenario development, workshop evaluation, and simulation. These stages contain the aspects and consequences of the reference model that has been explained in the introduction of this volume: (1) the involvement of stakeholder groups; (2) the multi-facet approach to the development and evaluation of telematics applications; (3) the important role of theoretical notions in the development process; (4) the importance of a multi-disciplinary team. A case study in the area of emergency medicine will be presented, focusing on the usefulness of mobile multimedia communications. Finally, conclusions will be drawn from the case study.     

Data mining and knowledge discovery in materials science and engineering: A polymer nanocomposites case study

In this study, data mining and knowledge discovery techniques were employed to validate their efficacy in acquiring information about the viscoelastic properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites solely from data derived from a designed experimental study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF weight fraction) and testing temperature were utilized as inputs and the storage modulus, loss modulus, and tan delta were selected as outputs. The data mining and knowledge discovery algorithms and techniques included self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature had the most significant effect on the output responses followed by VGCNF weight fraction. SOMs also showed how to prepare different VGCNF/VE nanocomposites with the same storage and loss modulus responses. A clustering technique, i.e., fuzzy C-means algorithm, was also applied to discover certain patterns in nanocomposite behavior after using principal component analysis as a dimensionality reduction technique. Particularly, these techniques were able to separate the nanocomposite specimens into different clusters based on temperature and tan delta features as well as to place the neat VE specimens (i.e., specimens containing no VGCNFs) in separate clusters. Most importantly, the results from data mining are consistent with previous response surface characterizations of this nanocomposite system. This work highlights the significance and utility of data mining and knowledge discovery techniques in the context of materials informatics.     

Toward an art and science of knowledge engineering: a case forbelief networks

The knowledge engineering of belief networks is discussed. Several design issues that arose during the construction of two belief network-based systems, Pathfinder and ARCO1, are described. The issues of accuracy, consistency, and calibration as they emerged during the design of these systems are addressed, and the ways in which compatibility of all networks designed for the same domain suggests an architecture for combining the recommendations of independently designed knowledge bases into a single, consensus recommendation are discussed     

Feminism and procedural content generation: toward a collaborative politics of computational creativity

ABSTRACT

Games now inhabit a space where creativity is no longer centered around human authorship. The use of procedural content generation has been embraced by industry, academics and fans as a means for reducing labor cost, providing additional replayable content for players, investigating computational creativity in a complex and multifaceted domain and enabling new kinds of playable experiences. This incorporation of computational creative labor confuses authorship, labor politics and responsibility for rhetoric embedded in the procedures by complicating the way in which the computer is portrayed to users, researchers and other developers. We can apply feminist methodologies attentive to questions of difference and power in systemic structures in order to better understand each of these questions in turn. This article presents an analysis of the post-anthropocentric phenomenon of computer creativity within games, via a feminist analysis of procedural content generating algorithms, its role in game design and its public portrayal.     

Expert systems in production planning and scheduling: A state-of-the-art survey

Intelligent solutions, based on expert systems, to solve problems in the field of production planning and scheduling are becoming more and more widespread nowadays. Especially the last decade has witnessed a growing number of manufacturing companies, including glass, oil, aerospace, computers, electronics, metal and chemical industries—to name just a few—interested in the applications of expert systems (ESs) in manufacturing. This paper is a state-of-the-art review of the use of ESs in the field of production planning and scheduling. The paper presents famous expert systems known in the literature and current applications, analyzes the relative benefits and concludes by sharing thoughts and estimations on ESs future prospects in this area.     

Automation and Orchestration of Zero Trust Architecture:Potential Solutions and Challenges

Zero trust architecture(ZTA)is a paradigm shift in how we protect data,stay connected and access resources.ZTA is non-perimeter-based defence,which has been emerging as a promising revolution in the cyber security field.It can be used to continuously maintain security by safeguarding against attacks both from inside and outside of the network system.However,ZTA automation and orchestration,towards seamless deployment on real-world networks,has been limited to be reviewed in the existing literature.In this pa-per,we first identify the bottlenecks,discuss the background of ZTA and compare it with traditional perimeter-based security architec-tures.More importantly,we provide an in-depth analysis of state-of-the-art AI techniques that have the potential in the automation and orchestration of ZTA.Overall,in this review paper,we develop a foundational view on the challenges and potential enablers for the automation and orchestration of ZTA.     

Capturing and reusing knowledge in engineering change management: A case of automobile development

The development of complex products, such as automobiles, involves engineering changes that frequently require redesigning or altering the products. Although it has been found that efficient management of knowledge and collaboration in engineering changes is crucial for the success of new product development, extant systems for engineering changes focus mainly on storing documents related to the engineering changes or simply automating the approval processes, while the knowledge that is generated from collaboration and decision-making processes may not be captured and managed easily. This consequently limits the use of the systems by the participants in engineering change processes. This paper describes a model for knowledge management and collaboration in engineering change processes, and based on the model, builds a prototype system that demonstrates the model’s strengths. We studied a major Korean automobile company to analyze the automobile industry’s unique requirements regarding engineering changes. We also developed domain ontologies from the case to facilitate knowledge sharing in the design process. For achieving efficient retrieval and reuse of past engineering changes, we used a case-based reasoning (CBR) with a concept-based similarity measure.

Picbreeder: a case study in collaborative evolutionary exploration of design space

For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potential. Picbreeder is an online community in which users can evolve and share images, and most importantly, continue evolving others' images. Through this process of branching from other images, and through continually increasing image complexity made possible by the underlying neuroevolution of augmenting topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC system. This paper discusses not only the strengths of the Picbreeder approach, but its challenges and shortcomings as well, in the hope that lessons learned will inform the design of future CIE systems.     

Strategic knowledge acquisition: a case study of learning through prototyping

To understand useful strategies for guiding the design process, we analyze the relationship between the use of prototypes and design knowledge acquisition. Prototyping in five design projects at a science museum is analyzed and compared. We also extract situations in which design knowledge is acquired from prototyping.     

Participation and common knowledge in a case study of student blogging

The interaction between participation and the emergence of common knowledge is the subject matter of this paper. A case study of a single class provides the focal point of analysis. During the semester the students participated in a blogging activity. As a result of their participation, the students create and distribute knowledge. The online efforts of the students can be described as participation in both a discourse and knowledge community. At one level, blogging is an activity composed of writing, reading, and commenting, and at a second level, the students share their thoughts in their own voices. At a third level, over the course of the semester, the student posts and commentary form a commons of information that can be mined later in the semester for other kinds of learning activities. Knowledge creation, distribution, and accumulation are analyzed in terms of student participation at both the level of individual events and from the perspective of an ongoing community.     

Field study on requirements engineering: Investigation of artefacts, project parameters, and execution strategies

Context

Requirements Engineering (RE) is a critical discipline mostly driven by uncertainty, since it is influenced by the customer domain or by the development process model used. Volatile project environments restrict the choice of methods and the decision about which artefacts to produce in RE.

Objective

We aim to investigate RE processes in successful project environments to discover characteristics and strategies that allow us to elaborate RE tailoring approaches in the future.

Method

We perform a field study on a set of projects at one company. First, we investigate by content analysis which RE artefacts were produced in each project and to what extent they were produced. Second, we perform qualitative analysis of semi-structured interviews to discover project parameters that relate to the produced artefacts. Third, we use cluster analysis to infer artefact patterns and probable RE execution strategies, which are the responses to specific project parameters. Fourth, we investigate by statistical tests the effort spent in each strategy in relation to the effort spent in change requests to evaluate the efficiency of execution strategies.

Results

We identified three artefact patterns and corresponding execution strategies. Each strategy covers different project parameters that impact the creation of certain artefacts. The effort analysis shows that the strategies have no significant differences in their effort and efficiency.

Conclusions

In contrast to our initial assumption that an increased effort in requirements engineering lowers the probability of change requests or project failures in general, our results show no statistically significant difference between the efficiency of the strategies. In addition, it turned out that many parameters considered as the main causes for project failures can be successfully handled. Hence, practitioners can apply the artefact patterns and related project parameters to tailor the RE process according to individual project characteristics.     

Diagnostic expert systems: a method for engineering knowledge used in sequential diagnosis

The paper presents a method for helping knowledge engineers in modelling the knowledge involved in sequential diagnosis. In particular, we consider sequential diagnosis as a process which occurs in the following type of scenario: (1) there exist some candidate hypotheses which are to be pursued; (2) for each hypothesis there are some alternative tests which might be executed to pursue it; (3) the available knowledge about the world precludes projecting into the future (in other words, the available knowledge about the world does not afford the information needed for planning sequences of tests, i.e. for projecting the consequences of possible sequences of actions and picking the best sequence); (4) the choice of the next test (or tests) is made on the basis of the current state of knowledge; (5) the outcome of a test execution is affected by uncertainty. The suggested method addresses the problem of engineering the knowledge experts use for making decisions under uncertainty. A practical example of the method is also presented: at any time of the diagnostic process the expert is provided with a dynamically updated list of suggested tests in order to support him or her in the decision-making problem about which test to execute next.     

Natural interaction in intelligent spaces: Designing for architecture and entertainment

The rapid evolution of computers’ processing power, progress in projection and display technology, and their low cost, accompanied by recent advances in mathematical modeling, make available to space designers today sophisticated technologies which were once accessible only to research institutions or large companies. Thanks to wireless sensing techniques it is possible to endow a space with perceptual intelligence, and make it aware of how people use it, move in it, or react to it. Intelligent Spaces are relevant for several applications or tasks which range from surveillance to entertainment, from medical rehabilitation to artistic performance, from museum exhibit design to commerce. The author’s work focuses on Narrative Spaces which are storytellers, able to articulate an informative or entertaining audio-visual narration for people interactively. Narrative Spaces communicate by use of large scale coordinated projections, sounds and displays whose contents are choreographed by the natural body movements or physical gestures of the people in them. This paper describes the guiding principles and modeling approaches that, according to the author, enable a robust modeling of user input and communication strategies for digital content presentation in Intelligent Narrative Spaces. It then provides examples of applications built according to the specified criteria.

Guidelines for conducting and reporting case study research in software engineering

Case study is a suitable research methodology for software engineering research since it studies contemporary phenomena in its natural context. However, the understanding of what constitutes a case study varies, and hence the quality of the resulting studies. This paper aims at providing an introduction to case study methodology and guidelines for researchers conducting case studies and readers studying reports of such studies. The content is based on the authors’ own experience from conducting and reading case studies. The terminology and guidelines are compiled from different methodology handbooks in other research domains, in particular social science and information systems, and adapted to the needs in software engineering. We present recommended practices for software engineering case studies as well as empirically derived and evaluated checklists for researchers and readers of case study research.     

Predicting the use and effectiveness of an office automation system (OAS): a case study

A model for predicting the use and effectiveness of an implemented office automation system (OAS) is presented and applied to a case study. The data was gathered via questionnaires (n=276) administered to a representative sample of employees working in an Italian telecommunication company. The model was tested using a structural equation confirmatory approach. Structural techniques test both the construct validity of measures and the predictive relations among constructs. Results showed satisfactory levels of construct validity of measures; the model proposed predicted significant amount of variance on the criterion constructs. The predictive model proposed is supported by the data; implications are discussed and perspectives are outlined.     

Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping

Land-cover mapping is an important research topic with broad applicability in the remote-sensing domain. Machine learning algorithms such as Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) have been playing an important role in this field for many years, although deep neural networks are experiencing a resurgence of interest. In this article, we demonstrate early efforts to apply deep learning-based classification methods to large-scale land-cover mapping. Based on the Stacked Autoencoder (SAE), one of the deep learning models, we built a classification framework for large-scale remote-sensing image processing. We adjusted and optimized the model parameters based on our test samples. We compared the performance of the SAE-based approach with traditional classification algorithms including RF, SVM, and ANN with multiple performance analytics. Results show that the SAE classifier trained with an entire set of African training samples achieves an overall classification accuracy of 78.99% when assessed by test samples collected independently of training samples, which is higher than the accuracies achieved by the other three classifiers (76.03%, 77.74%, and 77.86% of RF, SVM, and ANN, respectively) based on the same set of test samples. We also demonstrated the advantages of SAE in prediction time and land-cover mapping results in this study.