This article presents a review of the platform configuration and dynamic of obstacle-surmounting unmanned ground vehicles (UGVs). For now, unmanned systems have emerged as a result of the rapid advancement of artificial intelligence and modern manufacturing techniques both domestically and internationally. The research on unmanned systems has been improved a lot. The UGV platform can execute transportation, recurring, and military tasks independently. For the high-level self-control, adaption, and maneuverability abilities, the UGV platform has been applied in the military, industry, and other special fields widely. The UGV platform usually performs tasks in an unstructured environment, so the all-terrain performance becomes a key factor restricting their operating efficiency and reliability. A brief literature review of the UGV platform is carried out in this article.

Structural and sequential ambidexterity are proved to be two prevalent approaches in managing tension between exploration and exploitation. Dominant studies have treated the two approaches as mutually exclusive but have provided less insight about their combination, and the organizational configurations that advance such combination, which is a major meaningful gap explored in the current study. This study aims to explore the configurations of organization design choices to combine structural and sequential approaches from a holistic perspective. We apply fuzzy-set qualitative comparative analysis (fsQCA) to analyze the empirical data collected from 102 firms in China. The results show that firms attain high ambidexterity with both separated and blended configurations. Blended ones demonstrate that the structural and sequential approaches can be combined in a way that one approach dominates and the other subordinates. Organizational design mechanisms regarding the configurations for combining structural and sequential approaches are concluded as multielements (complements and substitutes) and multilevels (fit and interaction). These findings are also interpreted through the Chinese ‘Yin-Yang’ framework, which introduces ‘Yin-Yang balancing’ into the ambidexterity literature.

The increased demand for customer-adapted product solutions shows an increasing trend of product variety, leading to an increased internal variety and therefore -costs. The concept of modularization provides apossible solution to this challenge by developing modular kits. Nevertheless, modularization methods to not lead to one individual modular kit, but to several alternatives. The decision of which alternative to implement can be crucial to the applying companys succes. During this decision-making both customer- and company perspectives need to be taken into account. This contribution is to present a simulation-based approach to support the decision making by using a model-based configuration system. Furthermore, as classical decision-making processes are based upon historical data, future aspects are usually not taken into account. In order to counteract this situation, this contribution intends to simulate as well future aspects impacting the modular product architecture. In this case, the simulation is used in order to evaluate the individual performances of a Design-for-Variety product architecture as opposed to a Design-for-Future-Robustness by applying this method to the example of customer-individual laser machines.

Extant literature remains inconclusive with regard to optimal structural designs for sustaining effective knowledge flows, boosting innovativeness, and achieving superior performance in organizations. We contribute to the ongoing debate on formal, informal, and ambidextrous configurations in the specific context of small high-tech innovators. Adopting an inductive approach to theory building, we explore the factors that account for the variation in knowledge-focused designs across sample firms. In our study, innovative ventures rely on pure formal and informal organizational designs but also attempt to mix both, suggesting that gains from ambidexterity are not ubiquitous. Our analysis unveils that the pursuit of a given structural configuration results from a set of operating contingencies and a deliberate managerial effort to align firm idiosyncrasies with desired strategic outcomes. We advance a grounded theory of knowledge-focused organizational design in small high-tech innovators and formulate propositions that may be tested in future inquiries in the field.

Faultline configurations in top management teams are of vital importance in predicting firm outcomes. Grounded in faultline theory, we hypothesise the positive effects of faultlines through the dual routes of coordination and information processing under conditions of various subgroup configuration types. Second-hand data from publicly traded Chinese information technology firms are used to test our hypotheses. The results demonstrate that TMT faultline strength is positively related to a firm’s short-term performance only when both the number and the balance of subgroups are high and is positively related to a firm’s innovation activities only when the number of subgroups is high and the balance of subgroups is low. This study contributes to faultline theory by enriching the connotation of faultlines with the configurational perspective and advancing the debate on the effects of team faultlines as we reveal the benefits of TMT faultlines.

Reuse has long been a major goal of the knowledge engineering community. We present a case study of the reuse of constraint knowledge acquired for one problem solver, by two further problem solvers. For our analysis, we chose a well-known benchmark knowledge base (KB) system written in CLIPS, which was based on the propose and revise problem-solving method and which had a lift/elevator KB. The KB contained four components, including constraints and data tables, expressed in an ontology that reflects the propose and revise task structure. Sufficient trial data was extracted manually to demonstrate the approach on two alternative problem solvers: a spreadsheet (Excel) and a constraint logic solver (ECLiPSe). The next phase was to implement ExtrAKTor, which automated the process for the whole KB. Each KB that is processed results in a working system that is able to solve the corresponding configuration task (and not only for elevators). This is in contrast to earlier work, which produced abstract formulations of the problem-solving methods but which were unable to perform reuse of actual KBs. We subsequently used the ECLiPSe solver on some more demanding vertical transport configuration tasks. We found that we had to use a little-known propagation technique described by Le Provost and Wallace (1991). Further, our techniques did not use any heuristic “fix”’ information, yet we successfully dealt with a “thrashing” problem that had been a key issue in the original vertical transit work. Consequently, we believe we have developed a widely usable approach for solving this class of parametric design problem, by applying novel constraint-based problem solvers to data and formulae stored in existing KBs.

In order to estimate the specific intrinsic volumes of a planar Boolean model from a binary image, we consider local digital algorithms based on weighted sums of 2×2 configuration counts. For Boolean models with balls as grains, explicit formulas for the bias of such algorithms are derived, resulting in a set of linear equations that the weights must satisfy in order to minimize the bias in high resolution. These results generalize to larger classes of random sets, as well as to the design-based situation, where a fixed set is observed on a stationary isotropic lattice. Finally, the formulas for the bias obtained for Boolean models are applied to existing algorithms in order to compare their accuracy.

Configuration of large-scale applications in an engineering context requires a modeling environment that allows the design engineer to draft the configuration problem in a natural way and efficient methods that can process the modeled setting and scale with the number of components. Existing configuration methods in artificial intelligence typically perform quite well in certain subareas but are hard to use for general-purpose modeling without mathematical or logics background (the so-called knowledge acquisition bottleneck) and/or have scalability issues. As a remedy to this important issue both in theory and in practical applications, we use a standard modeling environment like the Unified Modeling Language that has been proposed by the configuration community as a suitable object-oriented formalism for configuration problems. We provide a translation of key concepts of class diagrams to inequalities and identify relevant configuration aspects and show how they are treated as an integer linear program. Solving an integer linear program can be done efficiently, and integer linear programming scales well to large configurations consisting of several thousands components and interactions. We conduct an empirical study in the context of package management for operating systems and for the Linux kernel configuration. We evaluate our methodology by a benchmark and obtain convincing results in support for using integer linear programming for configuration applications of realistic size and complexity.

Product configuration is a major industrial application domain for constraint satisfaction techniques. Conditional constraint satisfaction problems (CCSPs) and feature models (FMs) have been developed to represent configuration problems in a natural way. CCSPs are like constraint satisfaction problems (CSPs), but they also include potential variables, which might or might not exist in any given solution, as well as classical variables, which are required to take a value in every solution. CCSPs model, for example, options on a car, for which the style of sunroof (a variable) only makes sense if the car has a sunroof at all. FMs are directed acyclic graphs of features with constraints on edges. FMs model, for example, cell phone features, where utility functions are required, but the particular utility function “games” is optional, but requires Java support. We show that existing techniques from formal methods and answer set programming can be used to naturally model CCSPs and FMs. We demonstrate configurators in both approaches. An advantage of these approaches is that the model builder does not have to reformulate the CCSP or FM into a classic CSP, converting potential variables into classical variables by adding a “does not exist” value and modifying the problem constraints. Our configurators automatically reason about the model itself, enumerating all solutions and discovering several kinds of model flaws.

This article describes the technical principles and representation of a constraint-based configuration method for work processes. Methods developed for the configuration of modular systems comprising components have traditionally adopted a representation where the properties and compatibility requirements are expressed as constraints associated with individual components. However, this representation does not accurately capture constraints on paths and subprocesses and is therefore unsuitable for process configuration. This article extends established constraint-based configuration methods with a constraint language for specifying properties of execution paths in work processes. A framework for semiautomated process customization is presented. It integrates the extended constraint language with a metamodel of the work processes in an organization and allows to adapt generic work processes to fit the requirements of specific development projects. Heuristic search methods are applied to build valid process configurations by incrementally resolving constraint violations. The declarative framework facilitates the adaptation of abstract work processes as well as the validation and repair of existing processes. The approach was developed in the context of a real-world system of complex design and development processes where it was shown that significant process improvements and reduction in effort required to edit process models can be achieved.

Knowledge-based configurators are supporting configuration tasks for complex products such as telecommunication systems, computers, or financial services. Product configurations have to fulfill the requirements articulated by the user and the constraints contained in the configuration knowledge base. If the user requirements are inconsistent with the constraints in the configuration knowledge base, users have to be supported in finding out a way from the no solution could be found dilemma. In this paper we introduce a new algorithm (PersDiag) that allows the determination of personalized diagnoses for inconsistent user requirements in knowledge-based configuration scenarios. We present the results of an empirical study that show the advantages of our approach in terms of prediction quality and efficiency.

We discuss n4 configurations of n points and n planes in three-dimensional projective space. These have four points on each plane, and four planes through each point. When the last of the 4n incidences between points and planes happens as a consequence of the preceding 4n−1 the configuration is called a ‘theorem’. Using a graph-theoretic search algorithm we find that there are two 84 and one 94 ‘theorems’. One of these 84 ‘theorems’ was already found by Möbius in 1828, while the 94 ‘theorem’ is related to Desargues’ ten-point configuration. We prove these ‘theorems’ by various methods, and connect them with other questions, such as forbidden minors in graph theory, and sets of electrons that are energy minimal.

In cases where a domain theory can be successfully expressed in a logical formalism and can be used to formulate a task in that domain in mathematical terms, the task of building sound knowledge-based systems is greatly facilitated. However, it is not immediately obvious how the design aspects of such tasks, where these are an important feature of problem solving, can be incorporated in this approach. Design issues differ from search problems in that there may be several choices, each valid in some sense, but not (necessarily) equally good or equally appropriate in the current context. A case study is described in which a methodology is used based on the development of proof plans. The ability to conduct research according to the Popperian framework of hypothesis, validation, testing, and modification in response to empirical evidence – the hypothetico-deductive approach – seems essential to any rigorous scientific endeavor. It is believed that proof planning is a method which readily exploits this inherently incrementalist approach and could prove to be a powerful tool in designing AI systems.

Customers can directly express their preferences on many options when ordering products today. Mass customization manufacturing thus has emerged as a new trend for its aiming to satisfy the needs of individual customers. This process of offering a wide product variety often induces an exponential growth in the volume of information and redundancy for data storage. Thus, a technique for managing product configuration is necessary, on the one hand, to provide customers faster configured and lower priced products, and on the other hand, to translate customers' needs into the product information needed for tendering and manufacturing. This paper presents a decision-making scheme through constructing a product family model (PFM) first, in which the relationship between product, modules, and components are defined. The PFM is then transformed into a product configuration network. A product configuration problem assuming that customers would like to have a minimum-cost and customized product can be easily solved by finding the shortest path in the corresponding product configuration network. Genetic algorithms (GAs), mathematical programming, and tree-searching methods such as uniform-cost search and iterative deepening A* are applied to obtain solutions to this problem. An empirical case is studied in this work as an example. Computational results show that the solution quality of GAs retains 93.89% for a complicated configuration problem. However, the running time of GAs outperforms the running time of other methods with a minimum speed factor of 25. This feature is very useful for a real-time system.

In part-selection problems, parts are selected from catalogs and connected to meet the following problem requirements: functionality, specifications, and constraints. This paper formally defines the part-selection problem, enumerates a set of design properties that are useful during a search for a design solution, and provides an algorithm for solving part-selection problems based on a novel set of operators for manipulating portions of the design space.