Deductive and Inductive Reasoning for Processing the Claims of Unsatisfied Customers

摘要

We report on the novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents such that we are uncertain about motivations and decision-making principles of some of these agents. Our reasoning setting for such domains includes 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 (with the elements of abductive and analogous reasoning) involves the previous experience with the agent, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing the claims of unsatisfied customers. The task is to predict the future actions 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 reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical and analogy-based reasoning.