The memory and computational demands of such algorithms are ill-suited for embedded agents. We present an online algorithm for learning the parameters of stochastic context free grammars that uses summary statistics computed from sentences. Tapir has constructs for defining and constructing messages, resources, sensors and actions. Briefly, actions use sensors, resources and other actions to get things done. Resources connect actions to effectors in the world.
Sensors connect actions to a representation of the world.
Finally, actions and sensors communicate with one another via messages. I will discuss and illustrate the Tapir language and its design. The physical world and the language that we use to describe it are full of structure. Very young children discover this structure with apparent ease. They somehow transform sensory information gathered while exploring their environment into knowledge that enables both successful planning and natural language communication, two of the defining characteristics of human intelligence.