Object Identification in a Bayesian Context

Object identification—the task of deciding that two observed objects are in fact one and the same object—is a fundamental requirement for any situated agent that reasons about individuals. Object identity, as represented by the equality operator between two terms in predicate calculus, is essentially a first-order concept. Raw sensory observations, on the other hand, are essentially propositional— especially when formulated as evidence in standard probability theory. This paper describes patterns of reasoning that allow identity sentences to be grounded in sensory observations, thereby bridging the gap. We begin by defining a physical event space over which probabilities are defined. We then introduce an identity criterion, which selects those events that correspond to identity between observed objects. From this, we are able to compute the probability that any two objects are the same, given a stream of observations of many objects. We show that the appearance probability, whic...

• Type: Conference
• Year: 1997
• Where: IJCAI
• Authors: Timothy Huang, Stuart J. Russell
• Keywords: Appearance Probability, IJCAI 1997, IJCAI 2007, Observed Objects, Sensory Observations