MineralMiner: An active sensing simulation environment

One important problem in multiagent systems is determining how to gather information through sensing to support agent reasoning. This problem commonly arises in real-world applications such as robotics, mixed initiative systems, and others. One promising solution is to use active sensing to explicitly reason about the benefits (e.g., information gain, accuracy) and costs (e.g., resource use, knowledge corruption) of sensing actions, then proactively sense to maximize benefits and/or minimize costs. However, properties of complex environments make active sensing more difficult, necessitating further research and evaluation before deploying such solutions. In this paper, we describe MineralMiner, a novel simulation environment that extends previous environments to provide eight common complex environment properties in order to enable the effective study of active sensing. These properties can be fine-tuned using several simulation parameters in order to properly mimic environments likely to occur in real-world applications, allowing for insightful and successful pre-deployment testing, evaluation, and debugging of active sensing solutions, as well as on-going research into active sensing. Furthermore, we describe how several applications of sensing problems benefiting from active sensing can be abstracted and studied within MineralMiner, demonstrating the breadth and depth of its applicability to active sensing research.