DIMACS TR: 2002-42
Using reasoning about dynamic domains and inductive reasoning for automatic processing of claims
Authors: Boris Galitsky and Dmitry Vinogradov
ABSTRACT
We report on the novel approach to modeling a dynamic domain with limited
knowledge. Such domain may include participating agents such that we are
uncertain about motivations and decision-making principles of some of
these
agents. Our model for such domain includes the deductive and inductive
components. The former component is based on situation calculus and
describes the behavior of agents with complete information. The latter
machine learning-based inductive component that involves its previous
experience in prediction the agents^Ò actions.
Suggested reasoning machinery is applied to the problem of processing
of
claims of unsatisfied customers. The task is to predict the future action
of
a participating agent (the company that has upset the customer) to
determine
the required course of actions to settle down the claim. We believe our
framework reflect the general setting of reasoning in a dynamic domains in
the conditions of uncertainty, merging analytical and analogy-based
reasoning.
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