Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while maintaining exact answers. In this paper, we investigate how lifting enables us to perform probabilistic inference for factor graphs containing factors whose potentials are unknown. We introduce the Lifting Factor Graphs with Some Unknown Factors (LIFAGU) algorithm to identify symmetric subgraphs in a factor graph containing unknown factors, thereby enabling the transfer of known potentials to unknown potentials to ensure a well-defined semantics and allow for (lifted) probabilistic inference.

Susan Beardslee, principal analyst at ABI Research, said that change is constant-- with the unknown factor being the speed at which it will occur. Change happens quickly in trucking, and the pace of change will only accelerate in the coming years, according to Susan Beardslee, principal analyst at ABI Research. Speaking at a session at Omnitrac's Outlook user conference in Dallas on Feb. 25, Beardslee said that social changes, such as an aging work force, urbanization, and traffic congestion, have long been pointed to by forecasters as pushing trucking toward change. Fleets have had to learn how to attract younger workers, she pointed out. But younger drivers are less likely to see the attraction of spending days at a time on the road and away from home, which the long-haul segment requires.