John Seiffertt

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Domains such as force protection require an effective decision maker to maintain a high level of situation awareness. A system that combines humans with neural networks is a desirable approach. Furthermore, it is advantageous for the calculation engine to operate in three learning modes: supervised for initial training and known updating, reinforcement for(More)
The Introduction to Computer Engineering course at the University of Missouri-Rolla provides a thorough understanding of basic digital logic analysis and design. The course covers: digital numbering systems, Boolean algebra, function minimization using Karnaugh maps (K-maps), memory elements, and sequential logic design. Students' grades are determined by(More)
Evolution, human and animal cognition, and the emergent coordination of systems of autonomous agents are among the areas of nature drawn upon for inspiration by the field of computational intelligence. Researchers are increasingly using these aspects of nature in the exploration of market interaction, both to develop more scientific knowledge about economic(More)
For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research involves the use of neural networks and Markov chains to process information from sources(More)
AbstrAct As the study of agent-based computational economics and finance grows, so does the need for appropriate techniques for the modeling of complex dynamic systems and the intelligence of the constructive agent. These methods are important where the classic equilibrium analytics fail to provide sufficiently satisfactory understanding. In particular, one(More)
The study of strategic interaction among a society of agents is often handled using the machinery of game theory. This research examines how a Markov Decision Process (MDP) model may be applied to an important element of repeated game theory: the iterated prisoner's dilemma. Our study uses a Markovian approach to the game to represent the problem of in a(More)
Backpropagation is the most widely used neural network learning technique. It is based on the mathematical notion of an ordered derivative. In this paper, we present a formulation of ordered derivatives and the backpropagation training algorithm using the important emerging area of mathematics known as the time scales calculus. This calculus, with its(More)
The time scales calculus, which includes the study of the alpha derivative, is an emerging key area in mathematics. We extend this calculus to Approximate Dynamic Programming. In particular, we investigate application of the alpha derivative, one of the fundamental dynamic derivatives of time scales. We present a alpha-derivative based derivation and proof(More)
We propose a new research organization management paradigm to increase throughput of projects by allowing researchers to choose their own projects through self-organization. Our methods draw upon the field of Agent-Based computational social science where Artificial Life and simulated societies have been used to study complex systems including economies and(More)
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