Robot Localization and Exploration with Agent-Centered Search
In this article, we describe agent-centered search and illu strate this planning paradigm with examples from robot localization and exploration. Agent-centered search method s interleave planning and plan execution and restrict planni ng to the part of the domain around the current state of the agent. They can execute actions in the presence of time constraints and often have a small sum of planning and plan-execution time, both because they trade-off planning and plan-execution time and because they allow agents to gather information early in nondeterministic domains, whi ch reduces the amount of planning they have to perform for unencountered situations. These advantages become importan t as more and more intelligent systems are interfaced with the world and have to operate autonomously in complex environments. Agent-centered search methods have been applied to a variety of domains, including traditional search, STRIPS type planning, moving-target search, planning with totall y and partially observable Markov decision processes models , reinforcement learning, and robot navigation. We discuss the design and properties of several agent-centered search methods, focusing on robot localization and exploration.