We study the amount of knowledge about the network that is required in order to efficiently solve a task concerning this network. The impact of available information on the effici...
Recent research in robot exploration and mapping has focused on sampling environmental hotspot fields. This exploration task is formalized by Low, Dolan, and Khosla (2008) in a se...
This paper addresses the problem of exploration and mapping of an unknown environment by multiple robots. The mapping algorithm is an on-line approach to likelihood maximization t...
Reid G. Simmons, David Apfelbaum, Wolfram Burgard,...
A central problem in learning in complex environmentsis balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of explora...
An explicit exploration strategy is necessary in reinforcement learning (RL) to balance the need to reduce the uncertainty associated with the expected outcome of an action and the...
Abstract— This paper presents the Discrete Search Leading continuous eXploration (DSLX) planner, a multi-resolution approach to motion planning that is suitable for challenging p...
The essence of exploration is acting to try to decrease uncertainty. We propose a new methodology for representing uncertainty in continuous-state control problems. Our approach, ...
Emergent behaviors in simulations require explanation, so that valid behaviors can be separated from design or coding errors. We present a taxonomy, to be applied to emergent beha...
Often remote investigations use autonomous agents to observe an environment on behalf of absent scientists. Predictive exploration improves these systems’ efficiency with onboa...
Space, vast lands and dungeons… It is no coincidence that Space War and Adventure are among the best known of the first computer games. Both clearly appeal to the player’s cur...