Siddhartha Agarwal

Multi-agent Based Architecture for Acknowledged System of Systems☆

Abstract Negotiation and collaboration issues in large organizations, with centralized control, across the globe, are becoming more complex with each passing day. The Acknowledged System of Systems (SoS) is a new approach that addresses some of these issues in a systematic, efficient manner. This paper proposes a hierarchical architectural framework to support Acknowledged SoS architecting and analysis for a Department of Defense (DoD) Acquisition process. A major challenge of the successful planning and evolution of an Acknowledged SoS is the lack of understanding of the impact of presence or absence of a system and its interface with another constituent system on the overall architecture. The agent based model (ABM) structure developed here provides Acknowledged SoS manager that has both ability to address the managerial issues as well as a decision making tool for SoS architecting. This paper offers a complete integration of the techniques used to represent SoS architectures. The work illustrates the modeling approach through a domain setting.

Fuzzy Optimization of Acknowledged System of Systems Meta-architectures for Agent based Modeling of Development☆

Abstract Acknowledged systems of systems (SoS) lie on a continuum between authoritarian central control and anarchy. The constituent systems are independent, with a life and purpose of their own. The systems require not only technical interfaces, but also social interactions, to create the SoS. A fuzzy optimization process may be used to select a desirable SoS configuration, but it may be unachievable due to the inability to persuade the systems to cooperate in the plan. Modeling the systems’ internal decision processes could help understand how to design better SoS architectures. This research used generic, modular modeling processes to examine two proposed SoS architectures and the impact of degree of cooperation on the suitability of the achieved SoS.

A Hybrid Genetic Algorithm and Particle Swarm Optimization with Type-2 Fuzzy Sets for Generating Systems of Systems Architectures☆

Abstract Both modeling and simulating a system of systems (SoS) are difficult due not only to a changing environment but also the unique behavior that is linked to different participating systems. Generating architectures for a SoS is a multi-objective optimization problem with large number of variables and constraints. The paper presents several of computational intelligence techniques that can generate SoS architectures, such as genetic algorithms (GA), and particle swarm optimization (PSO) combined with Type 2 Fuzzy logic nets. The Maritime search and rescue (SAR) was used as a SoS domain scenario to both implement and demonstrate the architecting methodology. SAR utilizes a variety of systems, including unmanned aerial vehicles (UAV), coordination command control, communication systems and other larger manned vessels. The proposed methodology delivers SoS architects of SAR missions the ability to exploit the interdependence among all systems as well as individual systems inherent characteristics to satisfy stakeholders’ desired attributes. The architect is thus able to design architectures that are robust, efficient, net-centric, and affordable.

Executable Architectures Using Cuckoo Search Optimization Coupled with OPM and CPN-A Module: A New Meta-Architecture Model for FILA SoS

Understanding System of Systems (SoS) requires novel ways to apply systems engineering processes. Acknowledged SoS have recognized objectives, a designated manager and resources for the SoS. The goal of this research is to develop a proof of concept tool suite for Acknowledged SoS systems simulation. This suite is named flexible, intelligent and learning architectures for System of Systems (FILA-SoS). FILA-SoS assists the SoS manager in architecture generation, selection, and implementation working as an aid for decision making. Binary cuckoo search constrained optimization is used to generate meta-architectures which are evaluated by a fuzzy assessor for quality assurance. The architecture is then converted into an executable structure using Object Process Methodology (OPM) and Colored Petri Nets (CPN). A hybrid methodology comprising of OPM and CPN approach is implemented for simulating the acquisition environment. Initial application for a Search and Rescue (SAR) SoS, consisting of 25 individual systems with ten capabilities gave promising results.

Adaptive Learning Model for Predicting Negotiation Behaviors through Hybrid K-means Clustering, Linear Vector Quantization and 2-Tuple Fuzzy Linguistic Model☆

Abstract Acknowledged System of systems (SoS) manager has no direct control over contributing systems yet they deliver capabilities required to meet the purpose of the SoS operating in an interdependent environment. Forming a joint capability by contribution requires both persuasion and negotiation between the SoS coordinator and each individual system. Negotiation here involves multiple parties, and multiple issues. The challenges here include predicting an opponent systems negotiation behavior and reaching a near optimal negotiation outcome based on the following three issues: performance demands made by the coordinator, monetary benefits in lieu of effort required, and the deadline assigned to prepare for the final SoS participation formation. The negotiation framework proposed involves an unsupervised clustering of the difference between offers from both parties on all three issues by clustering techniques. The clustering results so far indicate the presence of four prominent behaviors: selfish, semi-cooperative, opportunistic, and extremely selfish. The clustered data was used to train both a radial bass function network (RBFN) and a linear vector quantization network (LVQN) to predict future offers. Once trained, the SoS manager uses 2-tuple fuzzy linguistic representation model for decision making. A time-dependent strategy is used to generate a new offer by SoS manager to the systems.

Augmented Cognition in Human-System Interaction through Coupled Action of Body Sensor Network and Agent Based Modeling

Abstract This research proposes a conceptual model to make significant progress in augmented cognition. We intend to design a generic architecture to improve the collective situational awareness (SA) of a team of soldiers on the battlefield during combat effects, and cultural communications. It also establishes a training outline for future landscapes of a similar nature by providing convincing feedback. It will serve as architecture of a model, based on the current laboratory framework. This involves data analysis of the feedback from the neurophysiological body sensors (heart rate, skin temperature, breathing rate and others) placed on a wearable immersive training vest. It includes gesture tracking gloves and fixed spatial locators. Approximately 15 soldiers will be incorporated into the study. Various training scenarios will be designed to capture all possible body perturbations, movements, and interactions. A similar agent-based model (ABM) of the training environment will be created to augment the soldiers cognitive competencies. ABM will integrate social systems interaction complexity into the decision makin g capability of the soldier in real time. Automated feedback can be provided using the model developed in the event of any social faux pas via gesture tracking and monitoring cognitive capacity. Model can help discover novel talents and render knowledge during training scenarios.

Selecting Attributes, Rules, and Membership Functions for Fuzzy SoS Architecture Evaluation☆

Abstract The development of the FILA-SoS meta-architecture approach to acknowledged systems of systems (SoS) analysis allows a relatively unbiased method for exploring a potential SoS architecture space. This paper delves more deeply into the process of building the lists of desirable fuzzy attributes of a SoS, developing rules for combining attribute values to an overall assessment, and discovering membership function shapes that work well. A wide range of options exist for all the individual elements of SoS assessment. Some recommendations for finding an appropriate combination for the adjustable parameters of fuzzy assessment models through random architecture chromosome testing and iteration are developed.

Predicting Response of Risk-Seeking Systems During Project Negotiations in a System of Systems

During project negotiations, typically, the awarding agency seeks bids from multiple parties. Examples of this setting include firms that are no longer willing to produce parts in-house or an airport seeking contracts for renovation. Risk-seeking parties are those that agree to work with lower budgets and under shorter deadlines, whereas risk-averse parties exhibit the opposite behavior. This setting can be found in the context of system of systems (SoS), where the SoS coordinator (the firm) has access to behavior characteristics of individual systems (parties) and their current workload from past interactions. The problem we study is for the SoS coordinator to predict the response of the systems in terms of budgets, deadlines, and performance targets, in advance of obtaining the actual response. This prediction can help the SoS negotiate the best deal. We present a quantitative model that predicts this response. Our model employs Markov chains to capture dynamics of the project, which would result when a bid is won, to quantify the response. Furthermore, our model accounts for the risk-taking tendencies and agility of the firm. We also analyze mathematical properties and provide numerical results to illustrate how our model can be used in a negotiation process.

An Approach to Advance Higher Order Cross-Cultural Awareness in Dismounted Soldiers

Abstract Cultural awareness is important for a range of operations from disaster assistance to active conflict. This paper illustrates the application of an acknowledged system of systems (SoS) to train dismounted soldiers in cultural awareness. Such training system is required to not only increase intelligence gathering opportunities but also minimize troop member loss during missions. Moreover, given the increase in the cultural interaction of dismounted soldiers, specific training is needed to raise cultural awareness. In this research an acknowledged (SoS) model of augmented cultural awareness centered on both agent-based simulation and body sensor networks is proposed. The immersive vest developed at Missouri University of Science and Technology helps assess the distributive intelligence (situational awareness) of both the dismounted soldier and civilians alike. This processed data is then used to train a similar prototype of the physical system environment as an agent based simulation. Strategies in agents behavior are also described. This description allows him/her to adapt specific behaviors in response to the foreign civilian.