Reinforcement learning (RL) is the study of systems that learn from interaction with their environment. The current framework of Reinforcement Learning is based on receiving scalar rewards, which the agent aims to maximize. But in many real world situations, tradeoffs must be made among multiple objectives. This necessitates the use...
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...
Building intelligent assistants has been a long-cherished goal of AI, and many were built and fine-tuned to specific application domains. In recent work, a domain-independent decision-theoretic model of assistance was proposed, where the task is to infer the user’s goal and take actions that minimize the expected cost of the...