The concept of emergent behavior is difficult to define, but can be considered as higher-level activity created by the individual actions of a population of simple agents. A potential means to model such behavior has been previously developed using Alfred North Whitehead's concept of Actual Entities. In computational form, actual entities are agents which evolve over time in response to interactions with their environment via the process of prehension. This occurs within the context of a Colony of Prehending Entities, a framework for implementation of AE concepts. This thesis explores the practical application of this framework in analysis of medical images, with specific focus on Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans. Specialized Slice COPEs are developed for analysis of individual image slices from these scans, focusing on the detection and segmentation of structures of interest (such as bone matter, ventricular tissue, and tumors). These structures exist in 3D and can be extracted across multiple consecutive scan slices. Therefore, a specialized Scan COPE is also proposed which aims to render the structure's volume via interpolation between previously analyzed slice images. The software developed for the specified application also provides visualization of a COPE's evolution toward its goal. This has additional value in general study of the COPE framework and the emergent behavior it generates.

This thesis' focus is on the use of Alfred North Whitehead's concept of Actual Entities as a computational tool for computer science and the introduction of a novel usage of Actual Entities as learning agents. Actual Entities are vector based agents that interact within their environment through a process called prehension. It is the combined effect of multiple Actual Entities working within a Colony of Prehending Entities that produces emergent, intelligent behavior. It is not always the case that prehension functions for desired behavior are known beforehand and frequently the functions are too complex to construct by hand. Through the use of Artificial Neural Networks and a technique called Observational Intelligence, Actual Entities can extract the characteristic behavior of observable phenomena. This behavior is then converted into a functional form and generalized to provide a knowledge base for how an observed object interacts with its surroundings.