Human exploration patterns in unknown, time-sensitive environments

Exploration of unknown environments has numerous applications in the domains of search and rescue, pursuit-evasion, map building, and military espionage and intelligence gathering. While it is ideal to have autonomous robots search intelligently based on global patterns and features of each environment, such learning algorithms should also be informed by, and perform at least as well as, human exploration. Most autonomous exploration algorithms focus on local and greedy choices and are less concerned with learning to recognize structural features of environments in order to improve global exploration performance. If the strategies people use during exploration can be determined, they can be used as a basis for creating good utility functions for autonomous robots to be optimized by machine learning techniques. Here, we perform a study where human participants explore both random and patterned environments, in two different time-sensitive scenarios, in an attempt to determine what strategies people use to maximize area explored under a variety of cases. We found that participants in a controlled study did in fact adapt their exploration strategies in time-sensitive scenarios and also exploited features of the environments in order to achieve better global exploration performance.