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

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

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

On the Use of Optimization Techniques for Strategy Definition in Multi Issue Negotiations

Στην παρούσα διπλωματική εργασία αναλύεται το πρόβλημα της λήψης απόφασης σε συστήματα αυτόματων διαπραγματεύσεων. Σκοπός είναι να σχεδιαστεί ένας αποδοτικός αλγόριθμος βάσει του οποίου οι πράκτορες λογισμικού θα δρουν σε ένα σενάριο ταυτόχρονων διαπραγματεύσεων.Οι πράκτορες δεν έχουν καμία πληροφόρηση για τα χαρακτηριστικά των αντιπάλων.Οι διαπραγματεύσεις πραγματοποιούνται με απώτερο στόχο την ανταλλαγή προϊόντων μεταξύ αγοραστών και πωλητών με συγκεκριμένα ανταλλάγματα. Κάθε προϊόν χαρακτηρίζεται από μια ομάδα χαρακτηριστικών. Για παράδειγμα, ένα προϊόν μπορεί να χαρακτηριζεται από την τιμή, από το χρόνο παράδοσης, κλπ. Κάθε αγοραστής αντιστοιχίζεται στις αυτόματες διαπραγματεύσεις με έναν αριθμό πωλητών. Προτείνουμε αλγόριθμους που προσπαθούν να επιλύσουν το πρόβλημα προσέγγισης αβεβαιότητας με τελικό σκοπό τη μεγιστοποίηση της ανταμοιβής των χρηστών. Η ανταμοιβή υπολογίζεται ως το άθροισμα με τα αντίστοιχα βάρη των χαρακτηριστικών. Εστιάζουμε στην πλευρά του αγοραστή και ορίζουμε μεθοδους για τον υπολογισμό των βαρών που επηρεάζουν τη χρησιμότητα του χρήστη. Πιο συγκεκριμένα, προτείνουμε μεθόδους για την αλλαγή της στρατηγικής του αγοραστή με στόχο να προσεγγίσουμε την καλύτερη συμφωνία. Ακόμα, χρησιμοποείται ο αλγόριθμος της θεωρία του σμήνους (Particle Swarm Optimization Algorithm) ώστε μέσω της κίνησης στο Ν-διαστατο χώρο να συγκλίνουν οι πράκτορες λογισμικού στη βέλτιστη συμφωνία. Παρουσιάζεται, τέλος, ένας αριθμός από πειράματα για τις προτεινόμενες μεθόδους για να αξιολογηθεί η απόδοσή τους και να συγκριθούν τα αποτελέσματα με τη σχετική βιβλιογραφία. In this thesis, we deal with the problem of decision making in automated negotiations. We consider the case where software agents undertake the responsibility of representing their owners in such negotiations. The final aim is to provide an efficient algorithm in which software agents will act in a scenario of concurrent negotiations. Agents have no knowledge on the opponents’ characteristics. Negotiations are held for the exchange of products between buyers and sellers with specific returns. Each product is characterized by a set of issues. For example, a product could be characterized by its price, delivery time, and so on. The buyer is involved in concurrent negotiations with a number of sellers. We propose algorithms that try to solve the problem of handling the uncertainty with the final aim of maximizing the entities rewards. The reward is calculated as a weighted sum of the discussed issue values. We focus on the buyer side and define specific methodologies for defining the weights that affect the utility of the buyer. Moreover, we propose a methodology for changing the strategy of the buyer in order to reach the optimal agreement. We are based on the widely known Particle Swarm Optimization (PSO) algorithm that is implemented by software agents’ movements in N-dimensional space to reach the optimal solution. We present a number of experiments for the proposed methodologies that show their performance and we compare our results with results found in the literature

Flexible and Intelligent Learning Architectures for SOS (FILA-SoS)

Multi-faceted systems of the future will entail complex logic and reasoning with many levels of reasoning in intricate arrangement. The organization of these systems involves a web of connections and demonstrates self-driven adaptability. They are designed for autonomy and may exhibit emergent behavior that can be visualized. Our quest continues to handle complexities, design and operate these systems. The challenge in Complex Adaptive Systems design is to design an organized complexity that will allow a system to achieve its goals. This report attempts to push the boundaries of research in complexity, by identifying challenges and opportunities. Complex adaptive system-of-systems (CASoS) approach is developed to handle this huge uncertainty in socio-technical systems

A Multi-Agent Architecture for the Design of Hierarchical Interval Type-2 Beta Fuzzy System

This paper presents a new methodology for building and evolving hierarchical fuzzy systems. For the system design, a tree-based encoding method is adopted to hierarchically link low dimensional fuzzy systems. Such tree structural representation has by nature a flexible design offering more adjustable and modifiable structures. The proposed hierarchical structure employs a type-2 beta fuzzy system to cope with the faced uncertainties, and the resulting system is called the Hierarchical Interval Type-2 Beta Fuzzy System (HT2BFS). For the system optimization, two main tasks of structure learning and parameter tuning are applied. The structure learning phase aims to evolve and learn the structures of a population of HT2BFS in a multiobjective context taking into account the optimization of both the accuracy and the interpretability metrics. The parameter tuning phase is applied to refine and adjust the parameters of the system. To accomplish these two tasks in the most optimal and faster way, we further employ a multi-agent architecture to provide both a distributed and a cooperative management of the optimization tasks. Agents are divided into two different types based on their functions: a structure agent and a parameter agent. The main function of the structure agent is to perform a multi-objective evolutionary structure learning step by means of the Multi-Objective Immune Programming algorithm (MOIP). The parameter agents have the function of managing different hierarchical structures simultaneously to refine their parameters by means of the Hybrid Harmony Search algorithm (HHS). In this architecture, agents use cooperation and communication concepts to create high-performance HT2BFSs. The performance of the proposed system is evaluated by several comparisons with various state of art approaches on noise-free and noisy time series prediction data sets and regression problems. The results clearly demonstrate a great improvement in the accuracy rate, the convergence speed and the number of used rules as compared with other existing approaches

Intelligent Business Process Optimization for the Service Industry

The company's sustainable competitive advantage derives from its capacity to create value for customers and to adapt the operational practices to changing situations. Business processes are the heart of each company. Therefore process excellence has become a key issue. This book introduces a novel approach focusing on the autonomous optimization of business processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm Optimization