Stakeholder-oriented Elaboration of Artificial Intelligence use cases using the example of Special-Purpose engineering

Artificial Intelligence (AI) offers high potential for addressing various challenges in engineering. The supportive use of cognitive systems allows an efficient division of work, especially for knowledge workers. For example, error-prone, repetitive, and non-essential activities can be outsourced or supported by AI. However, the establishment of AI solutions often fails due to a purely technically oriented approach. Successful implementation requires a prior selection of potential and intended benefits, in which all stakeholders are involved systematically. Including stakeholders at an early stage prevents expensive mistakes. In this paper human-oriented methods are applied and adapted to further detail AI use cases and achieve a high benefit for multiple stakeholders. That is the reason why this method stands out from previous methods. Five steps for elaboration AI use cases are presented in this method: Stakeholder-Identification, Stakeholder-Analysis, Synthesis of the user problem, Testing and Benchmarking, and Detailed evaluation and prioritization. In Special-Purpose engineering, the focus is on individual products for the customer. Therefore, three AI use cases have emerged from the design department of a Special-Purpose engineering company. The content of detailed descriptions and initial demonstrators are discussed with the stakeholders along the method and the results are fed back into a reusable, comprehensive architecture framework including a Black-Box model. In addition, the technical side is detailed and its applicability in a company is examined. Subsequently, the use cases are further adapted and evaluated with the users. The result is an AI use case that can proceed to the next phase of implementation. The following paper illustrates this stakeholder-oriented procedure for evaluating and detailing AI use cases validated by three use cases - for example, "Reverse Engineering of functions"- in Special-Purpose engineering

Strategy planning for collaborative humanoid soccer robots based on principle solution

The final publication is available at Springer via http://dx.doi.org/10.1007/s11740-012-0416-4[EN] Collaborative humanoid soccer robots are currently under the lime light in the rapidly advancing research area of multi-robot systems. With new functionalities of software and hardware, they are becoming more versatile, robust and agile in response to the changes in the environment under dynamic conditions. This work focuses on a new approach for strategy planning of humanoid soccer robot teams as in the RoboCup Standard Platform League. The key element of the approach is a holistic system model of the principle solution encompassing various strategies of a soccer robot team. The benefits of the model-based approach are twofold¿it enables intuitive behavioral specification of the humanoid soccer robots in line with the team strategies envisaged by the system developers, and it systematizes the realization of their collaborative behaviors based on the principle solution. The principle solution is modeled with the newly developed specification technique CONSENS for the conceptual design of mechatronic and self-optimizing systems.The specification technique CONSENS was developed in the course of the Collaborative Research Center 614 ‘‘Self-Optimizing Concepts and Structures in Mechanical Engineering’’ funded by the German Research Foundation (DFG) under grant number SFB 614. The first two authors are funded by the Ministry of Higher Education Malaysia under the grant number 600-RMI/ST/ FRGS 5/3/Fst (256/2010) and 600-RMI/ERGS 5/3 (23/2011).Low, CY.; Aziz, N.; Aldemir, M.; Dumitrescu, R.; Anacker, H.; Mellado Arteche, M. (2013). Strategy planning for collaborative humanoid soccer robots based on principle solution. Production Engineering. 7(1):23-34. https://doi.org/10.1007/s11740-012-0416-4S233471Asada M, Kitano H (1999) The RoboCup challenge. Rob Auton Syst 29:3–12Spaan MTJ, Groen FCA (2002) Team coordination through roles, positioning and coordinated procedures. RoboCupLau N, Lopes LS, Corrente G, Nelson F (2009) Multi-robot team coordination through roles, positionings and coordinated procedures. In: 2009 IEEE/RSJ international conference on intelligent robots and systems, October 11–15, St. Louis, USAIocchi L, Nardi D, Piaggo M, Sgorbissa A (2003) Distributed coordination in heterogeneous multi-robot systems. Auton Robots 15:155–168Almeida F, Lau N, Reis LP (2010) A survey on coordination methodologies for simulated robotic soccer teams, multi-agent logics, languages, and organisations federated workshops (MALLOW 2010). Lyon, FranceLückel J, Hestermeyer T, Liu-Henke X (2001) Generalization of the Cascade principle in view of structured form of mechatronic systems. In: IEEE/ASME international conference on advanced intelligent mechatronics (AIM 2001), Villa Olmo, Como, ItalyInternational Council on Systems Engineering (INCOSE) (2007) Systems engineering vision 2020. Incose-TP-2004-004-02, SeptemberGausemeier J, Frank U, Donoth J, Kahl S (2009) Specification technique for the description of self-optimizing mechatronic systems. Res Eng Des 20(4):201–223Cyberbotics Ltd., Webots overview. 20 September 2012 at http://www.cyberbotics.com/overviewBirkhofer H (1980) Analyse und Synthese der FunktionenTechnischerProdukte. Dissertation, TechnischeUniversitätBraunschweigLanglotz G (2000) Ein Beitrag zur Funktionsstrukturentwicklung Innovativer Produkte. Dissertation, Institut fuerr Rechneranwendung in Planung und Konstruktion, Universitaet Karlsruhe, Shaker-Verlag, Band 2/2000, AachenPostel J (1980) User Datagram Protocol. RFC 760, USC/Information Sciences Institut

SUPPORTING SYSTEMS ENGINEERING ACTIVITIES BY ARTIFACT-ORIENTED DESCRIPTION AND SELECTION OF METHODS

Systems Engineering (SE) is becoming increasingly relevant in industrial application since more stakeholders are involved in engineering activities. To implement SE, companies have to adapt existing engineering processes and methods. This adaption requires knowledge about new methods as well as their integration into the engineering activities. In order to ensure goal-oriented identification of methods for different SE activities in this contribution an action field profile and the Systems Engineering Method Matrix are proposed. The development of both tools is driven by the assumption that most SE activities and methods can be described based on the artefacts the deliver. In order to get feedback about the proposed tools, semi-structured interviews with two industry partners were conducted, focussing on the tool\u27s usability. These interviews underline the basic usability of the tools and their support to identify SE activities to be supported by (new) methods. Moreover, requirements for further development and adaption are derived from the interviews

Extreme drought impacts have been underestimated in grasslands and shrublands globally.

Climate change is increasing the frequency and severity of short-term (~1 y) drought events-the most common duration of drought-globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function-aboveground net primary production (ANPP)-was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought