Path propagation : a probabilistic inference algorithm for large and complex Bayesian networks

Bayesian networks are widely used for knowledge representation and uncertain reasoning. One of the most important services which Bayesian networks provide is (probabilistic) inference. Effective inference algorithms have been developed for probabilistic inference in Bayesian networks for many years. However, the effectiveness of the inference algorithms depends on the sizes of Bayesian networks. As the sizes of Bayesian networks become larger and larger in real applications, the inference algorithms become less effective and sometimes are even unable to carry out inference. In this thesis, a new inference algorithm specifically designed for large and complex Bayesian networks, called \u27path propagation\u27, is proposed. Path propagation takes full advantage of one of the most popular inference algorithms, i.e., global propagation. It improves over global propagation by carrying out inference only in certain paths in a junction tree that are relevant to queries. Compared with global propagation, path propagationtakes less computational resources and can effectively improve the computational efficiency for inference in large and complex Bayesian networks

Computer science: the hardware software and heart of IT

1st edition, 201

Wireless mosaic eyes based robot path planning and control. Autonomous robot navigation using environment intelligence with distributed vision sensors.

As an attempt to steer away from developing an autonomous robot with complex centralised intelligence, this thesis proposes an intelligent environment infrastructure where intelligences are distributed in the environment through collaborative vision sensors mounted in a physical architecture, forming a wireless sensor network, to enable the navigation of unintelligent robots within that physical architecture. The aim is to avoid the bottleneck of centralised robot intelligence that hinders the application and exploitation of autonomous robot. A bio-mimetic snake algorithm is proposed to coordinate the distributed vision sensors for the generation of a collision free Reference-snake (R-snake) path during the path planning process. By following the R-snake path, a novel Accompanied snake (A-snake) method that complies with the robot's nonholonomic constraints for trajectory generation and motion control is introduced to generate real time robot motion commands to navigate the robot from its current position to the target position. A rolling window optimisation mechanism subject to control input saturation constraints is carried out for time-optimal control along the A-snake. A comprehensive simulation software and a practical distributed intelligent environment with vision sensors mounted on a building ceiling are developed. All the algorithms proposed in this thesis are first verified by the simulation and then implemented in the practical intelligent environment. A model car with less on-board intelligence is successfully controlled by the distributed vision sensors and demonstrated superior mobility

Proceedings of the 9th MIT/ONR workshop on C3 Systems, held at Naval Postgraduate School and Hilton Inn Resort Hotel, Monterey, California June 2 through June 5, 1986

GRSN 627729"December 1986."Includes bibliographical references and index.Sponsored by Massachusetts Institute of Technology, Laboratory for Information and Decision Systems, Cambridge, Mass., with support from the Office of Naval Research. ONR/N00014-77-C-0532(NR041-519) Sponsored in cooperation with IEEE Control Systems Society, Technical Committee on C.edited by Michael Athans, Alexander H. Levis