Synthetic active collectives, composed of many nonliving individuals capable of cooperative changes in group shape and dynamics, hold promise for practical applications and for the elucidation of guiding principles of natural collectives. However, the design of collective robotic systems that operate effectively without intelligence or complex control at either the individual or group level is challenging. We investigate how simple steric interaction constraints between active individuals produce a versatile active system with promising functionality. Here we introduce the link-bot: a V-shape-based, single-stranded chain composed of active bots whose dynamics are defined by its geometric link constraints, allowing it to possess scale- and processing-free programmable collective behaviors. A variety of emergent properties arise from this dynamic system, including locomotion, navigation, transportation, and competitive or cooperative interactions. Through the control of a few link parameters, link-bots show rich usefulness by performing a variety of divergent tasks, including traversing or obstructing narrow spaces, passing by or enclosing objects, and propelling loads in both forward and backward directions. The reconfigurable nature of the link-bot suggests that our approach may significantly contribute to the development of programmable soft robotic systems with minimal information and materials at any scale.

Bot detection is an emerging problem in social games that requires different approaches from those used in massively multi-player online games (MMOGs). We focus on mouse selections as a key element of bot detection. We hypothesize that certain interface elements result in predictable differences in mouse selections, which we call spatial game signatures, and that those signatures can be used to model player interactions that are specific to the game mechanics and game interface. We performed a study in which users played a game representative of social games. We collected in-game actions, from which we empirically identified these signatures, and show that these signatures result in a viable approach to bot detection. We make three contributions. First, we introduce the idea of spatial game signatures. Second, we show that the assumption that mouse clicks are normally distributed about the center of buttons is not true for every interface element. Finally, we provide methodologies for using spatial game signatures for bot detection.