Recognition

Recognition of features and situations in complex dynamic environments is a pervasive problem for intelligent autonomous systems. It requires the application of both perceptual and domain knowledge. Learning such knowledge greatly increases the robustness of the system. Recognition research will focus on:

1. Supervised and unsupervised learning methods for clustering in feature space and the use of domain knowledge to guide feature construction;
2. The use of relational learning methods, especially inductive logic programming (pioneered by the partners), and Bayesian networks in building and maintaining representations of complex scenes.

Research in recognition will fundamentally advance the ability of agents to understand a scene or operating environment.

Recognition research will be led by Claude Sammut.