Pearce, A. R., Caelli, T., and Bischof, W. F. (1994). Efficient Spatial and Temporal Learning Procedures and Relational Evidence Theory. Transactions of the Western Australian Computer Science Symposium 1994, Perth, Australia, pp. 1-5.
We present a relational and evidence-based approach to building systems which can learn various identification, location and planning tasks in spatial and temporal domains. This machine learning problem is a difficult 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 different supervised identification and dynamic learning tasks in the spatial and temporal domain.
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