E cient Spatial and Temporal Learning Procedures and Relational Evidence Theory

We present a relational and evidence-based approach to building systems which can learn various identi cation, location and planning tasks in spatial and temporal domains. This machine learning problem is a di cult one because it involves, in addition to database operations such as indexing, the ability to generalize over training samples from continuous and relational data types. Relational evidence theory integrates methods from inductive logic programming with those from evidence theory and evaluates the symbolic representations formed. Generalization methods are combined with causal modeling and dynamic constraint satisfaction to optimize both the representation bias and search strategy used during learning. The approach is tested and compared with other machine learning techniques over several di erent supervised identi cation and dynamic learning tasks in the spatial and temporal domain.