They interleave planning and plan execution and often decrease the sum of planning and plan-execution time because gathering information early reduces the subsequent amount of planning needed. They only give advice about which actions to execute and fail gracefully if their advice is ignored from time to time. This makes it easy to integrate them into complete agent architectures. The analysis and development of agent-centered search methods combines ideas from robotics, artificial intelligence search, and theoretical aspects of graph search. We are both developing agent-centered search methods and analyzing when they work, why they work, and how well they work.
Ant robots are simple creatures with limited sensing and computational capabilities.
They have the advantage that they are easy to program and cheap to build. This makes it feasible to deploy groups of ant robots and take advantage of the resulting fault tolerance and parallelism. We study, both theoretically and in simulation, the behavior of ant robots for one-time or repeated coverage of terrain, as required for lawn moving, mine sweeping, and surveillance. Ant robots cannot use conventional planning methods due to their limited sensing and computational capabilities. To overcome these limitations, we study navigation methods that leave markings in the terrain, similar to what real ants do.