Hmm-based semantic learning for a mobile robot

Abstract

We are developing a intelligent robot and attempting to teach it language. While there are many aspects of this research, for the purposes of this dissertation the most important are the following ideas. Language is primarily based on semantics, not syntax, which is the focus in speech recognition research these days. To truly learn meaning, a language engine cannot simply be a computer program running on a desktop computer analyzing speech. It must be part of a more general, embodied intelligent system, one capable of using associative learning to form concepts from the perception of experiences in the world, and further capable of manipulating those concepts symbolically. This dissertation explores the use of hidden Markov models (HMMs) in this capacity. HMMs are capable of automatically learning and extracting the underlying structure of continuous-valued inputs and representing that structure in the states of the model. These states can then be treated as symbolic representations of the inputs. We show how a model consisting of a cascade of HMMs can be embedded in a small mobile robot and used to learn correlations among sensory inputs to create symbolic concepts, which can eventually be manipulated linguistically and used for decision making.