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Property-Driven Design for Robot Swarms: A Design Method Based on prescriptive modeling and Model Checking

ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS 2015年第4期9卷 17-17页

作者： Brambilla, Manuele Brutschy, Arne Dorigo, Marco Birattari, Mauro Univ Libre Bruxelles IRIDIA CoDE B-1050 Brussels Belgium

In this article, we present property-driven design, a novel top-down design method for robot swarms based on prescriptive modeling and model checking. Traditionally, robot swarms have been developed using a code-and-fix approach: in a bottom-up iterative process, the developer tests and improves the individual behaviors of the robots until the desired collective behavior is obtained. The code-and-fix approach is unstructured, and the quality of the obtained swarm depends completely on the expertise and ingenuity of the developer who has little scientific or technical support in his activity. Property-driven design aims at providing such scientific and technical support, with many advantages compared to the traditional unstructured approach. Property-driven design is composed of four phases: first, the developer formally specifies the requirements of the robot swarm by stating its desired properties;second, the developer creates a prescriptive model of the swarm and uses model checking to verify that this prescriptive model satisfies the desired properties;third, using the prescriptive model as a blueprint, the developer implements a simulated version of the desired robot swarm and validates the prescriptive model developed in the previous step;fourth, the developer implements the desired robot swarm and validates the previous steps. We demonstrate property-driven design using two case studies: aggregation and foraging.

关键词： Design Reliability Verification Swarm robotics top-down design prescriptive modeling model checking aggregation foraging

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Layered Behavior modeling via Combining Descriptive and prescriptive Approaches: A Case Study of Infantry Company Engagement

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第7期50卷 2551-2565页

作者： Bae, Jang Won Lee, Junseok Kim, Do-Hyung Lee, Kanghoon Lee, Jongmin Kim, Kee-Eung Moon, Il-Chul Elect & Telecommun Res Inst Smart Data Res Grp Daejeon 34129 South Korea Korea Adv Inst Sci & Technol Dept Ind & Syst Engn Daejeon 305701 South Korea Korea Adv Inst Sci & Technol Dept Comp Sci Daejeon 305701 South Korea

Defense modeling and simulation (DM&S) has brought insights into how to efficiently operate combat entities, such as soldiers and weapon systems. Most DM&S works have been developed to reflect accurate descriptions of military doctrines, yet these doctrines provide only guidelines of military operations, not details about how the combat entities should behave. Because such vague parts are often fulfilled with the appropriate behavior of combat entities in a battlefield, one part argues that DM&S should consider individual combat behaviors as well. However, it is known as an infeasible problem discovering best individual actions from infinite searching space, such as the battlefield. This paper proposes a layered behavior modeling to practically resolve this issue. The proposed method applies descriptive modeling to reduce the searching space by employing domain-specific knowledge;and prescriptive modeling to discover best individual actions in the reduced space. For the generalization, the proposed method adapts both modeling methods being modularized, and then the proposed method suggested an interface between them that is based on their semantic analogies. Both modeling methods are modularized, so they are interacted through an interface defined in the proposed method. This paper presents a realization of the proposed method through a case study of infantry company-level operations. In the case study, the proposed method is implemented with discrete event system specification formalism as the descriptive part and Markov decision process as the prescriptive part. The experimental results illustrated that the combat effectiveness resulted from the proposed method is statistically better than that from the descriptive-only modeling, and the difference would be guided by the objective of the combat behavior. Through the presented experimental results and the discussion, this paper argues that future DM&S should consider a broad spectrum from the battlefield incorporat

关键词： Analytical models Manuals Cognition Systematics Computational modeling Adaptation models Computer generated force (CGF) defense modeling and simulation (DM&S) descriptive modeling layered modeling prescriptive modeling

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Architecting in the Automotive Domain: Descriptive vs prescriptive Architecture 12

Architecting in the Automotive Domain: Descriptive vs Prescr...

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12th Working IEEE/IFIP Conference on Software Architecture (WICSA)

作者： Eliasson, Ulf Heldal, Rogardt Pelliccione, Patrizio Lantz, Jonn Volvo Car Grp Gothenburg Sweden Chalmers Dept Comp Sci & Engn Gothenburg Sweden Gothenburg Univ Dept Comp Sci & Engn S-41124 Gothenburg Sweden

ISBN: (纸本)9781479919222

To investigate the new requirements and challenges of architecting often safety critical software in the automotive domain, we have performed two case studies on Volvo Car Group and Volvo Group Truck Technology. Our findings suggest that automotive software architects produce two different architectures (or views) of the same system. The first one is a high-level descriptive architecture, mainly documenting system design decisions and describing principles and guidelines that should govern the overall system. The second architecture is the working architecture, defining the actual blueprint for the implementation teams and being used in their daily work. The working architecture is characterized by high complexity and considerably lower readability than the high-level architecture. Unfortunately, the team responsible for the high-level architecture tends to get isolated from the rest of the development organization, with few communications except regarding the working architecture. This creates tensions within the organizations, suboptimal design of the communication matrix and limited usage of the high-level architecture in the development teams. To adapt to the current pace of software development and rapidly growing software systems new ways of working are required, both on technical and on an organizational level.

关键词： automotive domain descriptive modeling empirical study industrial practice prescriptive modeling software architecture HIGH LEVEL ARCHITECTURE Software architecture ARCHITECTURE Building construction automotive engineering computer software empirical studies Software systems

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A predictive and prescriptive analytical framework for scheduling language medical interpreters

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HEALTH CARE MANAGEMENT SCIENCE 2021年第3期24卷 531-550页

作者： Ahmed, Abdulaziz Frohn, Elizabeth Univ Minnesota Business Dept Crookston MN 56716 USA Cloudbreak Healthath Bloomington MN USA

Although most hospitals in the United States provide medical services in English, a significant percentage of the U.S. population uses languages other than English. Mostly, the interpreting department in a hospital finds interpreters for limited English proficiency (LEP) patients, including inpatients, outpatients, and emergency patients. The department employs full-time and part-time interpreters to cover the demand of LEP patients. Two main challenges are facing an interpreting department: 1) there are many interpreting agencies in the market in which part-time interpreters can be chosen from. Selecting a part-time interpreter with the best service quality and lowest hourly rate makes the scheduling process difficult. 2) the arrival of LEP emergency patients must be predicted to make sure that LEP emergency patients are covered and to avoid any service delay. This paper proposes a framework for scheduling full-time and part-time interpreters for medical centers. Firstly, we develop a prediction model to forecast LEP patient demand in the emergency department (ED). Secondly, we develop a multi-objective integer programming (MOIP) model to assign interpreters to inpatient, outpatient, and emergency LEP patients. The goal is to minimize the total interpreting cost, maximize the quality of the interpreting service, and maximize the utilization of full-time interpreters. Various experiments are conducted to show the robustness and practicality of the proposed framework. The schedules generated by our model are compared with the schedules generated by the interpreting department of a partner hospital. The results show that our model produces better schedules with respect to all three objectives.

关键词： Interpreting service Multi-objective integer programming Predictive modeling prescriptive modeling Healthcare service Scheduling Operations research

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Social media optimization: Identifying an optimal strategy for increasing network size on Facebook

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2016年第PartA期59卷 15-25页

作者： Ballings, Michel Van den Poel, Dirk Bogaert, Matthias Univ Tennessee Dept Business Analyt & Stat Stokely Management Ctr 249 Knoxville TN 37996 USA Univ Ghent Dept Mkt B-9000 Ghent Belgium

This paper aims to create an expert system that yields an optimal strategy for increasing network size on Facebook. Data were obtained from 5488 Facebook users by means of a custom-built Facebook application. We computed a total of 426 variables. Using these data we estimated a predictive model of network size which is subsequently used in a prescriptive model. The former is estimated with Random Forest and the latter with a Genetic Algorithm. The results indicate that the proposed expert system can identify an optimal social media strategy. The system delivers concrete recommendations about, for example, the optimal time between status updates. The analysis reveals that network size can be increased by 61% if the optimal strategy is adopted. This study contributes to literature in the following two ways. First it devises a novel prescriptive social media expert system relying on an unprecedented variety of social media data. The results indicate that the system is effective and a viable strategic tool for increasing network size. Second it provides a list of the top drivers allowing future research to build similar systems efficiently. (c) 2015 Elsevier Ltd. All rights reserved.

关键词： Social media Facebook Strategy optimization Predictive modeling prescriptive modeling Random Forest Genetic Algorithm Data mining

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The 5th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2020) Special Edition on Dis/Misinformation Mining from Social media 20

The 5th International Workshop on Mining Actionable Insights...

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29th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Bagheri, Ebrahim Liu, Huan Shu, Kai Zarrinkalam, Fattane Ryerson Univ Toronto ON Canada Arizona State Univ Tempe AZ 85287 USA IIT Chicago IL 60616 USA

ISBN: (纸本)9781450368599

For the fifth edition of the workshop on Mining Actionable Insights from Social Networks (MAISoN), we organized a special edition with focus on dis/misinformation mining from social media, co-located with CIKM 2020. This topic has attracted a lot of interest from the community since the Coronavirus (COVID-19) epidemic has given rise to an increase of misinformation on social media. The aim of this edition was to bring together researchers from different disciplines interested in mining dis/misinformation on social media. In particular, the distinguishing focus of this special edition was its emphasis on techniques that use social media data for building diagnostic, predictive and prescriptive analysis models related to misinformation. This means that there is rigorous attention for techniques that can be used to understand how and why dis/misinformation is created and spread, to uncover hidden and unexpected aspects of dis/misinformation content, and to recommend insightful countermeasures to restrict the circulation of dis/misinformation and alleviate their negative effects.

关键词： Social media predictive modelling domain insights misinformation disinformation prescriptive modeling

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Unifying Explanatory and Constructive modeling Towards Removing the Gulf between Ontologies and Conceptual Models 16

Unifying Explanatory and Constructive Modeling Towards Remov...

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19th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS)

作者： Kuehne, Thomas Victoria Univ Wellington POB 600 Wellington 6140 New Zealand

ISBN: (纸本)9781450343213

The universal agreement regarding modeling as a useful endeavor can hide the large divide that runs through the modeling community. The differences between explanatory and constructive modeling give rise to two almost disjoint modeling universes, each based on different, mutually incompatible assumptions, rules, and tools. This division is undesirable as it prevents modelers from fluently transitioning between these worlds and denies them the benefits afforded by the underpinnings of the opposite camp. In this paper I characterize the typing disciplines underlying these different schools of thought, identify their respective trade-offs, and propose a unified approach which treats the different world views as modes of modeling that one may transition into in either direction. I present a unifying typing framework that can form the basis for a mutual fertilization between the hitherto rather separated worlds of explanatory versus constructive modeling.

关键词： descriptive modeling prescriptive modeling unification

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Federated learning approaches for fuzzy cognitive maps to support clinical decision-making in dengue

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Hoyos, William Aguilar, Jose Toro, Mauricio Univ Cordoba Grp Invest Microbiol & Biomed Cordoba Monteria Colombia Univ EAFIT Grp Invest I D I TIC Medellin Colombia Univ Andes Ctr Estudios Microelect & Sistemas Distribuidos Merida Venezuela IMDEA Networks Inst Leganes Madrid Spain

Federated learning is a distributed machine learning approach developed to guarantee the privacy and security of data stored on local devices. In healthcare, specifically in diseases of public health interest such as dengue, it is necessary to develop strategies that guarantee such data properties. Therefore, the aim of this work was to develop three federated learning approaches for fuzzy cognitive maps for the prediction of mortality and the prescription of treatment of severe dengue. The validation of the approaches was performed on severe dengue datasets from two dengue endemic regions in Colombia. According to the results, the use of federated learning significantly improves the performance of models developed in centralized environments. Additionally, the use of federated learning allows guaranteeing the privacy and security of each client's data due to the local training of the models. Federated learning is a useful tool in healthcare because it guarantees the privacy and security of patient data. Our results demonstrated the ability of aggregated models to predict mortality and prescribe treatment for severe dengue.

关键词： Fuzzy cognitive maps Federated learning Clinical decision-making Predictive modeling prescriptive modeling

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