Long-living autonomous agents must be able to learn to perform competently in
novel environments. One important aspect of competence is the ability to plan,
which entails the ability to learn models of the agent's own actions and their
effects on the environment. This thesis describes an approach to learn action
models of environments with continuous-valued spatial states and realistic
physics consisting of multiple interacting rigid objects. In such environments,
we hypothesize that objects exhibit multiple qualitatively distinct behaviors
based on their relationships to each other and how they interact. We call
these qualitatively distinct behaviors modes. Our approach models individual
modes with linear functions. We extend the standard propositional function
representation with learned knowledge about the roles of objects in
determining the outcomes of functions. Roles are learned as first-order
relations using the FOIL algorithm. This allows the functions modeling
individual modes to be "instantiated" with different sets of objects, similar
to relational rules such as STRIPS operators. We also use FOIL to learn
preconditions for each mode consisting of clauses that test spatial
relationships between objects. These relational preconditions naturally
capture the interaction dynamics of spatial domains and allow faster learning
and generalization of the model. The combination of continuous linear
functions, relational roles, and relational mode preconditions effectively
capture both continuous and relational regularities prominent in spatial
domains. This results in faster and more general action modeling in these
domains. We evaluate the algorithm on two domains, one involving pushing
stacks of boxes against frictional resistance, and one in which a ball
interacts with obstacles in a physics simulator. We show that our algorithm
learns more accurate models than locally weighted regression in the physics
simulator domain. We also show that relational mode preconditions learned with
FOIL are more accurate than continuous classifiers learned with support vector
machines and k-nearest-neighbor.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102383/1/jzxu_1.pd