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Belief Update in the pGOLOG Framework

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2174)

Abstract

High-level controllers that operate robots in dynamic, uncertain domains are concerned with at least two reasoning tasks dealing with the effects of noisy sensors and effectors: They have a) to project the effects of a candidate plan and b) to update their beliefs during on-line execution of a plan. In this paper, we show how the pGOLOG framework, which in its original form only accounted for the projection of high-level plans, can be extended to reason about the way the robot’s beliefs evolve during the on-line execution of a plan. pGOLOG, an extension of the high-level programming language GOLOG, allows the specification of probabilistic beliefs about the state of the world and the representation of sensors and effectors which have uncertain, probabilistic outcomes. As an application of belief update, we introduce belief-based programs, GOLOG-style programs whose tests appeal to the agent’s beliefs at execution time.

Keywords

Execution Time Belief State Reasoning Task Reply Action Primitive Action 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  1. 1.Department of Computer Science VAachen University of TechnologyAachenGermany

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