MIMICA: A GENERAL FRAMEWORK FOR SELF-LEARNING COMPANION AI BEHAVIOR

Companion or support characters controlled by Artificial Intelligence (AI) have been a feature of video games for decades. Many Role Playing Games (RPGs) offer a cast of support characters in the player’s party that are AI-controlled to various degrees. Many First Person Shooter (FPS) games include semi-autonomous or fully autonomous AI-controlled companions. Real Time Strategy (RTS) games have traditionally featured large numbers of semi-autonomous characters that collectively help accomplish various tasks (build, attack, etc.) for the player. While RPGs tend to focus on a single or a small number of well-developed character companions to accompany a player controlled main character, the RTS games tend to have anonymous and replaceable workers and soldiers to be micromanaged by the player. In this paper we present the MimicA framework, designed to govern AI companion behavior based on mimicking that of the player. Several features set this system apart from existing practices in AI-managed companions in contemporary RPG or RTS games. First, the behavior generated is designed to be fully autonomous, not partially autonomous as in most RTS games. Second, the solution is general. No specific prior behavior specifications are modeled. As a result, little to no genre, story or technical assumptions are necessary to implement this solution. Even the list of possible actions required is generalized. The system is designed to work independently of game representation. We further demonstrate, analyze and discuss MimicA by using it in Lord of Towers, a novel tower defense game featuring a player avatar. Through our user study we show that a majority of participants found the companions useful to them and liked the idea of this type of framework

CHARACTERIZATION OF INDIVIDUAL CHARGED Au25(SG)18 CLUSTERS AND THEIR ENHANCEMENT OF SINGLE MOLECULE MASS SPECTROMETRY

Metallic quantum clusters are stable structures that can exhibit many useful magnetic, chemical, and optical properties. Developing clusters for specific applications requires accurate methods for characterizing their physical and chemical properties. Most cluster characterization methods are ensemble-based measurements that can only measure the average values of the cluster properties. Single cluster measurements improve upon this by yielding information about the distribution of cluster parameters. This investigation describes the initial results on a new approach to detecting and characterizing individual gold nanoclusters (Au25(SG)18) in an aqueous solution with nanopore-based resistive pulse sensing. We also present a new application where the clusters are shown to increase the mean residence time of polyethylene glycol (PEG) molecules within an alpha hemolysin (αHL) nanopore. The effect appears over a range of PEG sizes and ionic strengths. This increases the resolution of the peaks in the single molecule mass spectrometry (SMMS) current blockade distribution and suggests a means for reducing the ionic strength of the nanopore solute in the SMMS protocol

Hiding in Plain Sight—The U.S. Navy and Dispersed Operations under EMCON, 1956–1972

As U.S. naval forces increasingly operate under the threat of antiship ballistic-missile attack while relying on rapid communication and information exchange, potential adversaries are likely to seek to target those forces and disrupt their communications and networks. Principles the U.S. Navy developed and practiced in the Cold War may still be workable responses