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Full-Text Articles in Physical Sciences and Mathematics
The Trust-Based Interactive Partially Observable Markov Decision Process, Richard S. Seymour
The Trust-Based Interactive Partially Observable Markov Decision Process, Richard S. Seymour
Theses and Dissertations
Cooperative agent and robot systems are designed so that each is working toward the same common good. The problem is that the software systems are extremely complex and can be subverted by an adversary to either break the system or potentially worse, create sneaky agents who are willing to cooperate when the stakes are low and take selfish, greedy actions when the rewards rise. This research focuses on the ability of a group of agents to reason about the trustworthiness of each other and make decisions about whether to cooperate. A trust-based interactive partially observable Markov decision process (TI-POMDP) is …
Self Organized Multi Agent Swarms (Somas) For Network Security Control, Eric M. Holloway
Self Organized Multi Agent Swarms (Somas) For Network Security Control, Eric M. Holloway
Theses and Dissertations
Computer network security is a very serious concern in many commercial, industrial, and military environments. This paper proposes a new computer network security approach defined by self-organized agent swarms (SOMAS) which provides a novel computer network security management framework based upon desired overall system behaviors. The SOMAS structure evolves based upon the partially observable Markov decision process (POMDP) formal model and the more complex Interactive-POMDP and Decentralized-POMDP models, which are augmented with a new F(*-POMDP) model. Example swarm specific and network based behaviors are formalized and simulated. This paper illustrates through various statistical testing techniques, the significance of this proposed …
A Trust-Based Multiagent System, Richard S. Seymour, Gilbert L. Peterson
A Trust-Based Multiagent System, Richard S. Seymour, Gilbert L. Peterson
Faculty Publications
Cooperative agent systems often do not account for sneaky agents who are willing to cooperate when the stakes are low and take selfish, greedy actions when the rewards rise. Trust modeling often focuses on identifying the appropriate trust level for the other agents in the environment and then using these levels to determine how to interact with each agent. Adding trust to an interactive partially observable Markov decision process (I-POMDP) allows trust levels to be continuously monitored and corrected enabling agents to make better decisions. The addition of trust modeling increases the decision process calculations, and solves more complex trust …
Speaker Recognition By Hidden Markov Models And Neural Networks, Erik J. Zeek
Speaker Recognition By Hidden Markov Models And Neural Networks, Erik J. Zeek
Theses and Dissertations
As humans, we develop the ability to identify people by their voice at an early age. Getting computers to perform the same task has proven to be an interesting problem. Speaker recognition involves two applications, speaker identification and speaker verification. Both applications are examined in this effort. Two methods are employed to perform speaker recognition. The first is an enhancement of hidden Markov models. Rather than alter some part of the model itself, a single-layer perceptron is added to perform neural post-processing. The second solution is the novel application of an enhanced Feature Space Trajectory Neural Network to speaker recognition. …
Generalized Hidden Filter Markov Models Applied To Speaker Recognition, John M. Colombi
Generalized Hidden Filter Markov Models Applied To Speaker Recognition, John M. Colombi
Theses and Dissertations
Classification of time series has wide Air Force, DoD and commercial interest, from automatic target recognition systems on munitions to recognition of speakers in diverse environments. The ability to effectively model the temporal information contained in a sequence is of paramount importance. Toward this goal, this research develops theoretical extensions to a class of stochastic models and demonstrates their effectiveness on the problem of text-independent (language constrained) speaker recognition. Specifically within the hidden Markov model architecture, additional constraints are implemented which better incorporate observation correlations and context, where standard approaches fail. Two methods of modeling correlations are developed, and their …