DIMACS TR: 2002-42

Using reasoning about dynamic domains and inductive reasoning for automatic processing of claims

Authors: Boris Galitsky and Dmitry Vinogradov


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^ 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.

Paper Available at: ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/2002/2002-42.ps.gz

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