Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
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We present an approach to building high-level control systems for robotics based on Bayesian decision theory. We show how this approach provides a natural and modular way of integrating sensing and planning. We develop a simple solution for a particular problem as an illustration. We examine the cost of using such a model and consider the areas in which abstraction can reduce this cost. We focus on one area, spatial abstraction, and discuss the design issues that arise in choosing an abstraction that we have used to solve problems involving robot navigation, and give a detailed account of the mapping from raw sensor data to the abstraction.