Computer Science

[This is the report derived from my MS project. This is the introduction, not the abstract. This project was conducted at North Carolina A&T State University]

What happens if the Internet were to crash, with all networks in deadlock or starved? It would cause billions, if not trillions, of dollars in economic damage. The survival of many businesses is entirely connected to the upkeep of the Internet. Therefore, the effort allocated towards the continuous development of the Internet is crucial to the stability of the global economy. This example shows how important it is to correctly develop concurrent systems like the Internet. And, like the Internet, weather and climate data is also an important aspect of the global economy. The ability to accurately predict future weather and climate trends is essential to allowing many companies to properly create contingency plans. For this project, the design of a system like SEAMONSTER (South East Alaska MOnitoring Network for Science, Telecommunications, Education, and Research) [15] will be verified. SEAMONSTER is a smart sensor web project that will be utilized for environment data collection of the Lemon Creek watershed in Juneau, Alaska along with sharing the data for research and education, which will help improve the development of sensor webs, that is, sensor networks in which the nodes coordinate actions.

First, the background information will be discussed, providing the basis for the project. Then, the project and its results will be explained, finishing with some final words on the project. Additional information is located in the appendix.

We present a fuzzy version of the crisp spatial logic developed by Randell et al., which takes the single relation connected−with as primitive. Membership functions are defined for each spatial relation defined in the crisp theory. Furthermore, principles are presented for defining linguistic variables whose linguistic values are spatial relations. The work reported here addresses spatial reasoning in situations where numerical or geometric precision is unlikely; it is particularly suited for dynamic situations.

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Recent technology advances in sensors and sensor networks have enabled large swarms of geographically distributed sensors and sensor networks to monitor various environmental attributes and allowed real-time sensor data sharing. However, these technological advances have also posed new challenges for managing distributed sensors, for processing large-amount of heterogeneous sensor data, and for sensor services to interoperate in a dynamic environment with bandwidth, power, time, and computational power constraints. In this paper, we present a service-oriented information framework to provide state-of-the-art mechanisms to access, integrate, analyze, and visualize geographically distributed sensor data. Workflow technologies are adopted to integrate and orchestrate various distributed sensor services to compose complex sensor services. A proof-of-concept pilot project is implemented to demonstrate the effectiveness and feasibility of this information framework.

Propositional dynamic logic (PDL) provides a natural setting for semantics of means-end relations involving non-determinism, but such models do not include probabilistic features common to much practical reasoning involving means and ends. We alter the semantics for PDL by adding probabilities to the transition systems and interpreting dynamic formulas α ϕ as fuzzy predicates about the reliability of α as a means to ϕ. This gives our semantics a measure of efficacy for means-end relations.

In this paper, we present first of all the working principles of an accuracy based learning classifier system. We also discuss the use of learning classifier systems for learning from data by considering a sample application. The sample application, the Terrain Reasoner Weight Adapter (TRWA), is a system that learns near optimal weights to be used by a path planner while generating routes. Manually generated weights are used to generate a sample data set for training the TRWA. We detail the TRWA and the significant improvements made to the usual XCS strategies in order to achieve our goal of using a supervised learning technique for the TRWA. A reward assignment scheme is developed. The use of tournament selection instead of roulette wheel selection for selecting two parents in the GA is also analyzed. The results obtained show the efficiency of the method.

We sketch a framework for learning structured coordinated behavior, specifically the tactical behavior of Experimental Unmanned Vehicles (XUVs). We conceptualize an XUV unit as a multiagent system (MAS) on which we impose a command structure to yield a holarchy, a hierarchy of holons, where a holon is both a whole and a part. The formalism used is a conservative extension of Statecharts, called a Parts/whole Statechart, which introduces a coordinating whole as a concurrent component on a par with the coordinated parts; wholes are related to common knowledge. We use X-classifier systems (XCSs). Exploiting Statechart semantics, we translate Statechart transitions into classifiers and define data structures that interact with an XCS.

Grid computing, which is characterized by large- scale sharing and collaboration of dynamic resources, is becoming an emerging computing platform on a global scale for data-intensive and computation-intensive application. However, the complications of large-scale scientific computations and simulations harnessing massive computing resources are compounded by extensive heterogeneity in environments arising from "the Grid." Scientists and engineers lack an intuitive grid-based

This paper demonstrates the utility of genetic algorithms (GAs) to determine a near optimal control strategy for electric propulsion systems. The various strategies implemented are simple GA, simple GA with elitism and micro GA. The accuracy and performance of the control strategy obtained using these methods are discussed along with their detailed description. This work inherently validates the use of ionic thrust for deep space missions.

The goal of this project is to examine an agent's environment in order to maximize its path planning strategy. Presented here is the application of fuzzy logic concepts to the area of robot motion planning. Specifically, the approximate cell decomposition strategy is utilized because it includes some similar characteristics found in fuzzy systems. Notably, the use of linguistic variables, membership functions, and fuzzy spatial logic are topics which provide a basis in examining the usefulness of applying fuzzy logic to the area of motion planning.

The objective of this research is to find a near optimal control strategy of an electric propulsion space vehicle to consume minimal fuel while traversing between two points in space. Micro Genetic Algorithms (GA) with the concept of Elitism is used to determine this optimal control.

We present an initial ontology for tactical behaviors conducted by unmanned ground vehicles (UGVs). We focus on activities, which are the denotations of verbs, notably 'move' but also 'look (for)' and several others. These take collective subjects, allowing activities to be attributed to units at various hierarchical levels. The semantics of verbs must consider the denotations of their grammatical complements; that is, we must consider entire verb frames. The thematic relations of the noun-phrase complements are critical, but prepositions also play an important role. FrameNet is an online lexical database of frames derived from text corpora. Our other major resource is Levin's classification of verbs according to how changes in their frames affect their meanings. Although natural languages have a large variety of words for aspects of tactical behaviors, there is motivation to get by with as few basic verbs as possible. A variety of meanings can often be associated with a verb by altering its frame, and we can impose co-reference constraints on combinations of frames to generate structures denoting more complex activities. A simple grammar is developed for the verbs of interest. Protégé-Frames ontologies include classes that inherit from linguistically inspired classes but capture domain-specific notions.

We discuss an Intelligence Analyst's Geospatial and Ontological Assistant (IAGOA) under development that associates an intelligence analyst's understanding of an agent's activities with the geospatial features of the area of operation where they take place. Activities are identified with frames for the corresponding verbs from the FrameNet lexical database. A modeler, using the FrameNet OWL distribution, produces software used by the analyst to update a KML file with annotations identifying instantiations of the frames elements of the relevant frames. The Google Earth API is used for rendering KML files and scripting. The agent is tracked and the analyst's conjecture of its activity is simulated; the analyst can redo her conjecture if need be. IAGOA's FrameNet-based approach instantiates concepts inherent in language, making explicit the activities and the constellation of role-fillers involved in these activities.