Learning the Cognitive Map and its Foundations

			   Benjamin Kuipers
		     Computer Science Department
		    University of Texas at Austin


William James [1890] wrote, ``The baby, assailed by eyes, ears, nose,
skin, and entrails at once, feels it all as one great blooming,
buzzing confusion.''  Similarly, we imagine a robot born into an
unknown environment with an unknown set of sensors and effectors.  How
can it first learn the properties of its sensorimotor system, and then
learn a useful cognitive map of its world?

Our Spatial Semantic Hierarchy [Kuipers, AIJ, 2000] provides the
target for this learning process.  The SSH is a hierarchy of different
representations for knowledge of space, with different expressive and
inferential capabilities.  The control level defines continuous
control laws linking locally distinctive states.  These patterns of
reliable continuous behavior are abstracted to causal schemas in which
states are linked by discrete actions, supporting the creation of
symbolic causal and topological maps.

The goal of our learning process is the identification of a reliable
set of perceptual features and primitive motor commands that can
support the definition of trajectory-following and hill-climbing
control laws.  Once we can define the SSH control level, the rest of
the cognitive map can be built on that foundation.  I will describe
work that solves this problem for a simple simulated robot, and
current directions of research with physical robots in real
environments.



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Benjamin Kuipers is Professor of Computer Sciences at the University
of Texas at Austin.  He investigates the representation of commonsense
and expert knowledge, with particular emphasis on the effective use of
incomplete knowledge.  He received the B.A. in Mathematics from
Swarthmore College, and the Ph.D. in Mathematics from MIT.  He has
held research or faculty appointments at MIT, Tufts University, and
the University of Texas.  His research accomplishments include
developing the TOUR model of spatial knowledge in the cognitive map,
the QSIM algorithm for qualitative simulation, Access-Limited Logic
for knowledge representation, and the Spatial Semantic Hierarchy model
of knowledge for robot exploration and mapping.  He served as
Department Chairman 1997-2001, and is a Fellow of AAAI and IEEE.

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