MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model

This dissertation adopts a holistic and detailed approach to modeling spatially explicit agent-based artificial intelligent systems, using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. The research questions that addresses stem from the need to understand and analyze the real-world patterns and dynamics of land use change from a coupled human-environmental systems perspective. Describes the systemic, mathematical, statistical, socio-economic and spatial dynamics of the MABEL modeling framework, and provides a wide array of cross-disciplinary modeling applications within the research, decision-making and policy domains. Establishes the symbolic properties of the MABEL model as a Markov decision process, analyzes the decision-theoretic utility and optimization attributes of agents towards comprising statistically and spatially optimal policies and actions, and explores the probabilogic character of the agents\u27 decision-making and inference mechanisms via the use of Bayesian belief and decision networks. Develops and describes a Monte Carlo methodology for experimental replications of agent\u27s decisions regarding complex spatial parcel acquisition and learning. Recognizes the gap on spatially-explicit accuracy assessment techniques for complex spatial models, and proposes an ensemble of statistical tools designed to address this problem. Advanced information assessment techniques such as the Receiver-Operator Characteristic curve, the impurity entropy and Gini functions, and the Bayesian classification functions are proposed. The theoretical foundation for modular Bayesian inference in spatially-explicit multi-agent artificial intelligent systems, and the ensembles of cognitive and scenario assessment modular tools build for the MABEL model are provided. Emphasizes the modularity and robustness as valuable qualitative modeling attributes, and examines the role of robust intelligent modeling as a tool for improving policy-decisions related to land use change. Finally, the major contributions to the science are presented along with valuable directions for future research

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents

Validation of Novel PLL-driven PI Control Schemes on Supporting VSIs in Weak AC-Connections

The integration of distributed energy resources (DERs) in modern power systems has substantially changed the local control capabilities of the grid since the majority of DERs are connected through a controlled dc/ac inverter interface. Such long-distance located DER installations, usually represented by current regulated dc sources, can inject large amounts of power into the main ac grid at points where the strength of the ac connection is low. The efficient and stable performance of such a power scheme is related to the capability of the control applied to retain the power extraction close to the maximum and simultaneously to regulate the dc-side voltage as well as the ac-side voltage magnitude at the weak ac connection point. This is implemented by designing the controllers of the voltage source inverters (VSIs) in a manner that reliably satisfies the above tasks. To this end, decentralized cascaded control schemes, driven by novel, locally implemented phase locked loops (PLLs), suitable to work in weak ac connections, are proposed for the VSI performance regulation by using new fast inner-loop proportional-integral (PI) current controllers. A decisive innovation is proposed by inserting an extra damping term in the inner-loop controllers to guarantee stability and convergence to the desired equilibrium. This is analytically proven by a rigorous analysis based on the entire nonlinear system model, where advanced Lyapunov-based methods are deployed in detail. As a good transient response of the VSI interface is indeed critical for the energy and grid system management, the conducted simulation and experimental results confirm that the proposed scheme efficiently supports the ac- and dc-side voltages of the VSI under different varying conditions in the power production or any voltage changes of the main grid

Vegetation indices: Advances made in biomass estimation and vegetation monitoring in the last 30 years

During the last 30 years Vegetation Indices (VI) have been extensively used for tracing and monitoring vegetation conditions, such as health, growth levels, production, water and nutrients stress, etc. In this paper the characteristics of over 20 VIs based on the VNIR spectrum are described in order to provide the reader with adequate material to form a picture of their nature and purpose. It is not, though, a review article due to the fact that a huge volume of work exists all over the world and a simple lining up of the related papers would not contribute to an understanding of the usefulness of VIs. A limited number of review work is included, together with research results from various operational and research applications of VI for wheat damage assessment in Northern Greece