Intelligent Assistants - A Decision-Theoretic Model

Building intelligent computer assistants has been a §long-cherished goal of§AI. Many intelligent assistant systems were built §and fine-tuned to specific§application domains. In this work,§we develop a general model of assistance that §combines§three powerful ideas: decision theory, hierarchical §task models and§probabilistic relational languages. We use the §principles of decision theory§to model the general problem of intelligent §assistance. We use a§combination of hierarchical task models and §probabilistic relational§languages to specify prior knowledge of the computer §assistant. The§assistant exploits its prior knowledge to infer the §user's§goals and takes actions to assist the user. We §evaluate the decision§theoretic assistance model in three different §domains including a real-world domain§to demonstrate its generality. We show through §experiments that both the§hierarchical structure of the goals and the §parameter sharing facilitated by§relational models significantly improve the learning §speed of the agent.§Finally, we present§the results of deploying our relational hierarchical §model in a real-world§activity recognition task.