Representation and reasoning for autonomous agents

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This research concerns the reasoning that a robot would have to do to interact properly with a world that is changing and incompletely known. Almost all the research in robotics today concerns basic level tasks such as dealing with noisy sensors (like sonar), controlling inexact manipulators (like robot arms), or determining how to move safely without colliding into people or obstacles. Higher level decisions, such as when a robot needs to move at all, in what order it should tackle its jobs, or what it should try to do if something goes wrong, usually end up being scripted in advance. The goal of the research proposed here is to investigate what it would take for an automated agent to be able to make such decisions for itself, based on it knowing what it is trying to do, what it knows about its world, and what it has been able to find out using its sensors. To get these decisions to come out of a general reasoning process, however, it will be necessary to express in a language suitable for automated reasoning, enough of what the agent needs to know about its world, goals, actions, ability, the cognitive states of other agents, collaborative task execution, and so on. The proposed research would examine various aspects of these problems, and attempt to develop high level agent controllers of this sort.