Immersion has become a key buzzword in the theme park industry, with many themed lands and attractions being designed with this objective in mind. This paper defines immersion through the concept of the ironic imagination and examines its role in theme park attractions. A literature review was first conducted to identify general design principles for the creation of immersive theme park attractions. Authentic settings that utilize all of the senses were considered first, along with a system of positive and negative cues for evaluating immersive experiences. The importance of simple and emotional stories was also addressed, before investigating the role that employees and guests play in an immersive attraction. Eight design principles were identified, and using these principles a blue sky design for an immersive theme park attraction was developed. An overview of the attraction is included and accompanied by an analysis of how the design principles were applied.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.