Gauge Theory on Fuzzy S^2 x S^2 and Regularization on Noncommutative R^4

We define U(n) gauge theory on fuzzy S^2_N x S^2_N as a multi-matrix model, which reduces to ordinary Yang-Mills theory on S^2 x S^2 in the commutative limit N -> infinity. The model can be used as a regularization of gauge theory on noncommutative R^4_\theta in a particular scaling limit, which is studied in detail. We also find topologically non-trivial U(1) solutions, which reduce to the known "fluxon" solutions in the limit of R^4_\theta, reproducing their full moduli space. Other solutions which can be interpreted as 2-dimensional branes are also found. The quantization of the model is defined non-perturbatively in terms of a path integral which is finite. A gauge-fixed BRST-invariant action is given as well. Fermions in the fundamental representation of the gauge group are included using a formulation based on SO(6), by defining a fuzzy Dirac operator which reduces to the standard Dirac operator on S^2 x S^2 in the commutative limit. The chirality operator and Weyl spinors are also introduced.Comment: 39 pages. V2-4: References added, typos fixe

AGENT MEETING SCHEDULER

This dissertation is purposed to record all the data gathered throughout author's study and research for this project. A deep study of agent algorithm is conducted based on current available agent meeting scheduler from combination of software agent and algorithm data structure knowledge. The current problem of typical meeting scheduler is it is time consuming and inefficient; and also a resource needs to be allocated to perform the meeting scheduling job. Agent meeting scheduler will be used to replace this typical meeting scheduler to make it more efficient in term of deciding meeting time. The study is meant to research and select suitable algorithm to be implemented in agent meeting scheduler. An agent meeting scheduler prototype then will be developed to prove that the selected algorithm is working properly. Qualitative research method is being used to gather necessary data on agent algorithm and this data will be used to select the suitable algorithm. Through the research conducted on available algorithm for agent meeting scheduler, genetic algorithm is selected to be used in this project. The agent meeting scheduler prototype then will be developed by using PHP language. PHP is selected for its interactivity and extensibility

Artificial Intelligence Research Branch future plans

This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems

Generalized Probabilistic Reasoning and Empirical Studies on Computational Efficiency and Scalability

Expert Systems are tools that can be very useful for diagnostic purposes, however current methods of storing and reasoning with knowledge have significant limitations. One set of limitations involves how to store and manipulate uncertain knowledge: much of the knowledge we are dealing with has some degree of uncertainty. These limitations include lack of complete information, not being able to model cyclic information and limitations on the size and complexity of the problems to be solved. If expert systems are ever going to be able to tackle significant real world problems then these deficiencies must be corrected. This paper describes a new method of reasoning with uncertain knowledge which improves the computational efficiency as well as scalability over current methods. The cornerstone of this method involves incorporating and exploiting information about the structure of the knowledge representation to reduce the problem size and complexity. Additionally, a new knowledge representation is discussed that will further increase the capability of expert systems to model a wider variety of real world problems. Finally, benchmarking studies of the new algorithm against the old have led to insights into the graph structure of very large knowledge bases

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,

A five-cycle living visual taxonomy of learning interactions

This paper describes my development of a useful, descriptive model that one-to-one practitioners could use to analyse transcripts of their sessions, design new strategies and even test them out. Further, this work has the potential to offer a framework that students, patients, clients and colleagues could use to communicate the types of interactions they prefer. The narrative in my educational life around the domain of heuristic generates a living-educational-theory as a values-based explanation for my educational influences as a tutor. The living contradictions I encounter, and praxes I make up to help me imagine solutions, are portrayed visually and verbally; and this leads to my proposal of a five-cycle living visual taxonomy of learning interactions. I consider the application of my living-educational-theory to other domains, for example, confidence; and to power dynamics, autism support, student engagement, expert behaviour, external influences, understanding negative feedback, and remoteness in heuristics. Interestingly, one future possibility is to use my taxonomy to develop a ‘positivist/scientific flavoured’ quantitative instrument to support learning analytics and educational data-mining; to optimise learning, and the environment in which it takes place

Fuzzy and neural control

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning

Modeling historic, current, and available aboveground forest biomass along the Missouri River corridor

"July 2014."Dissertation Supervisor: Dr. Hong S. He.Dissertation Supervisor: Dr. Shibu Jose.Includes vita.This research presents the culmination of statistical, landscape, and geospatial analyses that examine the geographic dynamics of aboveground forest biomass (AFB) within the Missouri River corridor, Missouri USA. The Missouri River corridor is a region specifically within Missouri that encompasses 106,000 km², and is regarded as a processing region for improving the viability of Missouri's biomass/biofuel industry. Current and historic forest inventory data coupled with remote sensing, edaphic, physiographic, and climate variables were integrated into an ensemble regression tree method, Random Forest (RF), to estimate AFB, determine external driving forces of AFB, and visualize geographic locations where the greatest deviations exist between current and historic AFB values. The applicability of constructing a hybrid modeling framework using RF was initially tested in Chapter 2 by estimating current (observed data derived from Forest Inventory and Analysis) and theoretical (based on 20% of AFB found within Missouri) AFB, and calculating the percent change to determine percent changes in AFB across the landscape. The third chapter extended the RF modeling procedure to include historical information derived from General Land Office (GLO) data to estimate a baseline measure of AFB. Current AFB was again estimated and then compared to historic values where an additional synthesis was performed to investigate the top predictors of AFB. The fourth chapter examined a fuzzy logic approach for developing a suitability index based on available AFB. Available AFB was determined by applying physical constraints onto estimated AFB from the RF model, which included forest transitions and distance to rivers. The model results failed to reject our null hypothesis that there were no differences between observed and predicted AFB, and x model accuracy was very low for all AFB estimate. Results from these investigations indicated that 1) the greatest potential for increasing AFB may be along the floodplains of the Missouri anIncludes bibliographical references (pages 123-137)