Theory-Based Inductive Learning: An Integration of Symbolic and
  Quantitative Methods
  The objective of this paper is to propose a method that will generate a
causal explanation of observed events in an uncertain world and then make
decisions based on that explanation. Feedback can cause the explanation and
decisions to be modified. I call the method Theory-Based Inductive Learning
(T-BIL). T-BIL integrates deductive learning, based on a technique called
Explanation-Based Generalization (EBG) from the field of machine learning, with
inductive learning methods from Bayesian decision theory. T-BIL takes as inputs
(1) a decision problem involving a sequence of related decisions over time, (2)
a training example of a solution to the decision problem in one period, and (3)
the domain theory relevant to the decision problem. T-BIL uses these inputs to
construct a probabilistic explanation of why the training example is an
instance of a solution to one stage of the sequential decision problem. This
explanation is then generalized to cover a more general class of instances and
is used as the basis for making the next-stage decisions. As the outcomes of
each decision are observed, the explanation is revised, which in turn affects
the subsequent decisions. A detailed example is presented that uses T-BIL to
solve a very general stochastic adaptive control problem for an autonomous
mobile robot.
