Towards a General Intelligent Information Agent: Framework, Models, and Evaluation

Abstract

Despite their common goal of assisting users in information access, current Information Retrieval (IR) systems, such as search engines and recommender systems, are studied and deployed separately across application contexts, resulting in scattered user information and fragmented support for a user's task. Can we develop a single general intelligent system to unify those specific systems for assisting users with information access using multiple modes (e.g., search, recommendation, and conversation)? In this perspective paper, we present a vision for developing a general intelligent information agent (GIANT) that not only unifies the current specific systems for supporting information access but also goes beyond information access to provide personalized task completion for users. We propose to formalize GIANT generally as an agent interacting with its users to minimize their effort on finishing a task as well as the agent's resource overhead. We propose a probabilistic modeling framework for optimizing GIANT's interactions with its users and discuss how to estimate its four component models, including Situation Model, Content Model, Task Model, and User Model. We discuss how the framework can be refined to derive specific interaction strategies and implemented with a general Markov Decision Process (MDP) architecture. We further discuss how to evaluate GIANT using user simulation and conclude with an outline of some promising directions for future research.