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A robot's actions are inherently stochastic, as its sensors are noisy and itsactions do not always have the intended effects. For this reason, the agentlanguage Golog has been extended to models with degrees of belief andstochastic actions. While this allows more precise robot models, the resultingprograms are much harder to comprehend, because they need to deal with thenoise, e.g., by looping until some desired state has been reached withcertainty, and because the resulting action traces consist of a large number ofactions cluttered with sensor noise. To alleviate these issues, we propose touse abstraction. We define a high-level and nonstochastic model of the robotand then map the high-level model into the lower-level stochastic model. Theresulting programs are much easier to understand, often do not require beliefoperators or loops, and produce much shorter action traces.

Using Abstraction for Interpretable Robot Programs in Stochastic Domains