Civilians on the Battlefield: Creating a Realistic Training Aid for the United States Military

The United States and our allies and partners have adopted a humane approach to warfare based on established principle of the laws of war centered on the principles of Military Necessity, Humanity, Proportionality, Distinction, and Honor. These principles dictate that US Military forces conduct warfare with a careful consideration of our impact on civilian populations with a special duty to protect and limit harm as much as possible given the accomplishment of a mission. Likewise, the US Military has developed a sound counterinsurgency and unified action military model that recognizes that warfare is not fought simply with kinetic force, but rather is conducted across an array of areas, including the battle for “hearts and minds” of civilian populations to assist with military actions and legitimize lawful governments. These two factors contribute to a steady requirement to train military forces to respond properly when confronted with civilians on the battlefield. Unfortunately, the only viable method to provide this training is to employ large numbers of role-players – either in a live training setting or controlling entities in a wargame. These role-players must either be hired or be tasked from other military units. There are currently no viable autonomous solutions. The result is that commanders often choose to forego this training as too costly – which could have serious long-term ramifications for military forces confronting civilians in the real world. Can agent based modelling accurately represent civilians confronted with military operations to provide realistic training for military leaders and Soldiers? This thesis investigates this question and develops an agent-based model to explore the answer

Enhancing Free-text Interactions in a Communication Skills Learning Environment

Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs