A Multi-Level Organization of Semantic Primitives for Learning Models of Environment Autonomously from Continuous Data for Design (extended abstract)

S. Prabhakar and G. Smith

In this paper, we present an overview of the project on Autonomous Learning Design Agents which began recently. An agent that addresses the task of Design by Autonomous Learning (DAL) builds an abstract model the environment from the sensory data with the goal to modify the environment to meet a new set of functionalities. Any agent that addresses the DAL task is confronted with a task of transforming the underlying causes that produce the continuous data of the environmento produce another set of continuous data. In our work, we explore the possibility of such a transformation through a hierarchical order of discretizations each of which allows the agent to act differently, and yet allows the agent to draw some global conclusions.

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