An English-Language Argumentation Interface for Explanation Generation with Markov Decision Processes in the Domain of Academic Advising

Abstract

A Markov Decision Process (MDP) policy presents, for each state, an action, which preferably maximizes the expected utility accrual over time. In this article, we present a novel explanation system for MDP policies. The system interactively generates conversational English-language explanations of the actions suggested by an optimal policy, and does so in real time. We rely on natural language explanations in order to build trust between the user and the explanation system, leveraging existing research in psychology in order to generate salient explanations. Our explanation system is designed for portability between domains and uses a combination of domain-specific and domain-independent techniques. The system automatically extracts implicit knowledge from an MDP model and accompanying policy. This MDP-based explanation system can be ported between applications without additional effort by knowledge engineers or model builders. Our system separates domain-specific data from the explanation logic, allowing for a robust system capable of incremental upgrades. Domain-specific explanations are generated through case-based explanation techniques specific to the domain and a knowledge base of concept mappings used to generate English-language explanations.