TheoryBased Inductive Learning: An Integration of Symbolic and Quantitative Methods
Spencer Star
Abstract:
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 TheoryBased Inductive Learning (TBIL). TBIL integrates deductive learning, based on a technique called ExplanationBased Generalization (EBG) from the field of machine learning, with inductive learning methods from Bayesian decision theory. TBIL 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. TBIL 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 nextstage 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 TBIL to solve a very general stochastic adaptive control problem for an autonomous mobile robot.
Keywords: TheoryBased Inductive Learning, EBG, Symbolic and Quantitative Methods
Pages: 401424
PS Link:
PDF Link: /papers/87/p401star.pdf
BibTex:
@INPROCEEDINGS{Star87,
AUTHOR = "Spencer Star
",
TITLE = "TheoryBased Inductive Learning: An Integration of Symbolic and Quantitative Methods",
BOOKTITLE = "Uncertainty in Artificial Intelligence 3 Annual Conference on Uncertainty in Artificial Intelligence (UAI87)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1987",
PAGES = "401424"
}

