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.