Bootstrap Learning of Foundational Representations

Common sense, and hence most other human knowledge, is built on knowledge of a few foundational domains, such as space, time, action, objects, causality, and so on. We are investigating how this knowledge can be learned from unsupervised sensorimotor experience.

We assume that an agent, human or robot, starts with a low-level ontology for describing its sensorimotor interaction with the world. We call this the "pixel level". William James called it the "blooming buzzing confusion". The learning task is to create useful higher-level representations for space, time, actions, objects, etc, to support effective planning and action in the world.

The basic idea behind bootstrap learning is to compose multiple machine learning methods, using weak but general unsupervised or delayed-reinforcement learning methods to create the prerequisites for applying stronger but more specific learning methods such as abductive inference or supervised learning.

An important common theme of all this work is the learning of a higher level ontology of places, objects, and their relationships, based on the low-level "pixel ontology" of direct experience. These learning methods create new symbols and categories, solving the symbol grounding problem for these symbols, and defining the symbols in terms of the agent's own experience, not the experience of an external teacher or programmer.

Selected Publications

The full set of papers on bootstrap learning is available.

Work described here has taken place in the Intelligent Robotics Lab at the Artificial Intelligence Laboratory, The University of Texas at Austin. Research of the Intelligent Robotics lab is supported in part by grants from the Texas Advanced Research Program (3658-0170-2007), the National Science Foundation (IIS-0413257, IIS-0713150, and IIS-0750011), and from the National Institutes of Health (EY016089).
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