Learning about Objects and Activities

Statistical learning has become a central theme in computational studies of intelligence, with important but largely independent developments in perceptual tasks like object recognition and in conceptual reasoning (Mitchell (1997)). Typically, the former involves clustering and regression over quantitative representations based on real linear spaces ( n R ), whereas the latter involves symbolic procedures over qualitative representations that emphasise the relationships between objects (e.g. next-to, occurs-before, overlaps, similar-shape). Integrating learning across these two levels of abstraction presents a number of challenges, particularly in relating quantitative and qualitative representations.