High-level robot programming in dynamic and incompletely known environments

Abstract

This thesis advocates the usefulness and practicality of a logic-based approach to AI and in particular to high-level control of mobile robots. The contribution of the research work reported here is twofold: 1) the development of theoretical frameworks that account for uncertainty and unmodeled dynamics in an environment where an acting agent has to achieve certain goals and 2) the implementation of the developed ideas on a mobile robot. We have elaborated the approach to designing efficient and reliable controllers in Golog following two different perspectives on the environment where the control program is supposed to operate. According to one perspective, investigated in Chapter 4, the agent has a logical model of the world, but there is no probabilistic information about the environment where the agent is planning to act, and the agent is not capable or has no time for acquiring probabilities of different effects of its actions. In this case, the uncertainty and dynamics of the environment can be accounted only by observing the real outcomes of actions executed by the agent, by determining possible discrepancies between the observed outcomes and the effects expected according to the logical model of the world and then by recovering, if necessary, from the