Computational intelligence based complex adaptive system-of-systems architecture evolution strategy

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta-architecture generation, architecture assessment and architecture implementation. Meta-architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. An SAR SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta-architectures based on the wave model. --Abstract, page iii

Cooperative agent-based SANET architecture for personalised healthcare monitoring

The application of an software agent-based computational technique that implements Extended Kohonen Maps (EKMs) for the management of Sensor-Actuator networks (SANETs) in health-care facilities. The agent-based model incorporates the BDI (Belief-Desire-Intention) Agent paradigms by Georgeff et al. EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize. Current results show a combinatorial approach to optimization with EKMs provides an improvement in event trajectory estimation compared to standalone cooperative EKM processes to allow responsive event detection for patient monitoring scenarios. This will allow healthcare professionals to focus less on administrative tasks, and more on improving patient needs, particularly with people who are in need for dedicated care and round-the-clock monitoring. ©2010 IEEE

Deployment of an agent-based SANET architecture for healthcare services

This paper describes the adaptation of a computational technique utilizing Extended Kohonen Maps (EKMs) and Rao-Blackwell-Kolmogorov (R-B) Filtering mechanisms for the administration of Sensor-Actuator networks (SANETs). Inspired by the BDI (Belief-Desire-Intention) Agent model from Rao and Georgeff, EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize, while the Rao-Blackwell filtering mechanism reduces the external noise and interference in the problem set introduced through the self-organization process. Initial results demonstrate that a combinatorial approach to optimization with EKMs and Rao-Blackwell filtering provides an improvement in event trajectory approximation in comparison to standalone cooperative EKM processes to allow responsive event detection and optimization in patient healthcare

Best matching processes in distributed systems

The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individuals—from clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory. The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies

Security in Data Mining- A Comprehensive Survey

Data mining techniques, while allowing the individuals to extract hidden knowledge on one hand, introduce a number of privacy threats on the other hand. In this paper, we study some of these issues along with a detailed discussion on the applications of various data mining techniques for providing security. An efficient classification technique when used properly, would allow an user to differentiate between a phishing website and a normal website, to classify the users as normal users and criminals based on their activities on Social networks (Crime Profiling) and to prevent users from executing malicious codes by labelling them as malicious. The most important applications of Data mining is the detection of intrusions, where different Data mining techniques can be applied to effectively detect an intrusion and report in real time so that necessary actions are taken to thwart the attempts of the intruder. Privacy Preservation, Outlier Detection, Anomaly Detection and PhishingWebsite Classification are discussed in this paper

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

A reconfigurable distributed multiagent system optimized for scalability

This thesis proposes a novel solution for optimizing the size and communication overhead of a distributed multiagent system without compromising the performance. The proposed approach addresses the challenges of scalability especially when the multiagent system is large. A modified spectral clustering technique is used to partition a large network into logically related clusters. Agents are assigned to monitor dedicated clusters rather than monitor each device or node. The proposed scalable multiagent system is implemented using JADE (Java Agent Development Environment) for a large power system. The performance of the proposed topology-independent decentralized multiagent system and the scalable multiagent system is compared by comprehensively simulating different fault scenarios. The time taken for reconfiguration, the overall computational complexity, and the communication overhead incurred are computed. The results of these simulations show that the proposed scalable multiagent system uses fewer agents efficiently, makes faster decisions to reconfigure when a fault occurs, and incurs significantly less communication overhead. The proposed scalable multiagent system has been coupled with a scalable reconfiguration algorithm for an electric power system attempting to minimize the number of switch combination explored for reconfiguration. The reconfiguration algorithm reconfigures a power system while maintaining bus voltages within limits specified by constraints

Agents enacting social roles: balancing formal structure and practical rationality in MAS design

Der Soziologe Pierre Bourdieu zeigt die feine und häufig ignorierte Unterscheidung zwischen theoretischer Rationalität und der 'Logik der Praxis' auf. Diese Differenz, so die Annahme der Autoren, ist sowohl bei dem Versuch, menschliche Organisationen mit Robustheit und Flexibilität auszustatten, als auch bei jeder Bemühung, Informationssysteme auf der Basis von Mechanismen organisatorischer Koordination zu modellieren, zu berücksichtigen. Im INKA-Projekt, einem Bestandteil des deutschen Forschungsprogramms Sozionik, bildet diese Ansicht den Ausgangspunkt. Computergesteuerte Agenten, die sich selbst koordinieren und in einer Weise eigenständig agieren, ahmen im Prinzip menschliche Akteure in organisatorischen Umgebungen nach. Dabei müssen sie mit der Spannung zurechtkommen, die aus den formalen vorgegeben Beschreibungen der Organisation und den strukturierten Erwartungen, welche sich von den täglichen Interaktionen auf dem Level der Produktionsstätten ableiten, resultiert. In der Soziologie besteht eine Möglichkeit, diese Spannung in der Rollentheorie zu konzeptionieren, die auf verschiedene Formen der Darstellung von formalen Rollenbeschreibungen und praktischen Rollen ausgerichtet ist. Außerdem ist gemäß der Organisationstheorie und empirischen Untersuchungen bekannt, dass in der realen Welt die täglichen Aus- und Verhandlungen der Arbeitnehmer eine Form des Arbeitshandelns darstellen. Basierend auf diesen Betrachtungen orientiert sich das INKA-Projekt an zwei Hauptzielen: (1) Modellierung und Implementierung eines technischen Systems, in dem die Agenten fähig sind, sich auf der Basis von praktischen Rollen mittels Verhandlung selbständig zu koordinieren; (2) Entwicklung einer Annäherung an die Erforschung hybrider Sozialität, die bei dem Eintritt solcher Agenten in menschliche Organisationen entsteht. Die Ausführungen beginnen mit einer kurzen Diskussion der konzeptionellen Probleme, die auftreten, wenn Computerprogramme auf praktische Relationen oder auf soziologische Konzepte von praktischen Modalitäten der Interaktion, des Problemlösens und Planens zugeschnitten sind. Dies führt zu der Formulierung von drei generellen Herausforderungen innerhalb des Sozionik-Programms. Im Anschluss wird in einige Details der soziologisch basierten Schaffung von praktischen Rollen und aushandelnden Agenten eingeführt. Es folgt die Darstellung der Grundstruktur für die entsprechende MAS-Architektur. Dann werden zwei generelle Probleme behandelt, die bei dem derzeitigen Entwicklungsstand des vorgestellten Projekts und nach Ansicht der Autoren im gesamten Sozionik-Programm auftreten. Es wird ein integrierter Ansatz vorgeschlagen, der alle Aktivitäten in Sozionik-Systemen in einer systematischen Weise korreliert. Des Weiteren wird ein methodisches Instrument zur Erforschung der Hybridisierung präsentiert. Der Text schließt mit der Darstellung einiger konzeptioneller Erweiterungen und zukünftiger Arbeitsschritte. (ICGÜbers)Sociologist Pierre Bourdieu pointed out the subtle and often ignored difference between theoretical rationality and the 'logic of practice'. This difference, we will argue, has to be taken in account when trying to capture the robustness and flexibility of human organizations, and is especially important for any effort to model information systems on mechanisms of organizational coordination. In the INKA-project, part of the German Socionics program, we took this insight as our very starting point. Computational agents that 'act' and coordinate themselves in a way that at least mimics in principle human actors in organizational environments have to cope with the tension between the formal descriptions given by the organization at large and the structured expectations that derive from their daily interactions on the shop-floor level. In sociology one way of conceptualizing this tension is role theory, focusing on the different forms of enactment of formal role descriptions and practical roles. Furthermore, from organizational theory and empirical investigations we know that in the 'real world' daily negotiations by the employees themselves are one way of working around the incoherences of formal prescriptions, job descriptions and work schedules. Based on these considerations the INKA-project is oriented by two main objectives: To model and implement a technical system in which the agents are capable of coordinating themselves via negotiating on the basis of practical roles, and to develop an approach for the investigation of hybrid sociality that emerges if those agents are re-entered into human organizations. The contribution begins with a brief discussion of the conceptual problems that occur if computer programs are to be modeled on practical relations or on sociological concepts of practical modes of interaction, problem solving and planning; this leads us to the formulation of three general challenges within the Socionics program (2). In the next part we introduce in some detail our sociologically grounded modeling of practical roles and negotiating agents (3), and our framework for a corresponding MAS-architecture (4). Then we turn to two general issues, that came up at the present state of development in our project - and, as we assume, in the entire Socionics program. We propose an integrated approach that correlates all activities in Socionic systems development in a systematic way (5), and we present a methodological instrument for the investigation of hybridization (6). We conclude by sketching some conceptual extensions and further working steps (7)

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon