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Dialogue systems are designed to offer human users social support or functional services through natural language interactions. Traditional conversation research has put significant emphasis on a system’s response-ability, including its capacity to understand dialogue context and generate appropriate responses. However, the key element of proactive behavior—a crucial aspect of intelligent conversations—is often overlooked in these studies. Proactivity empowers conversational agents to lead conversations towards achieving pre-defined targets or fulfilling specific goals on the system side. Proactive dialogue systems are equipped with advanced techniques to handle complex tasks, requiring strategic and motivational interactions, thus representing a significant step towards artificial general intelligence. Motivated by the necessity and challenges of building proactive dialogue systems, we provide a comprehensive review of various prominent problems and advanced designs for implementing proactivity into different types of dialogue systems, including open-domain dialogues, task-oriented dialogues, and information-seeking dialogues. We also discuss real-world challenges that require further research attention to meet application needs in the future, such as proactivity in dialogue systems that are based on large language models, proactivity in hybrid dialogues, evaluation protocols and ethical considerations for proactive dialogue systems. By providing a quick access and overall picture of the proactive dialogue systems domain, we aim to inspire new research directions and stimulate further advancements towards achieving the next level of conversational AI capabilities, paving the way for more dynamic and intelligent interactions within various application domains. 

Emerging building analytics rely on data-driven machine learning algorithms. However, writing these analytics is still challenging— developers need to know not only what data are required by the analytics but also how to reach the data in each individual building, despite the existing solutions to standardizing data and resource management in buildings. To bridge the gap between analytics development and the specific details of reaching actual data in each building, we present Energon, an open-source system that enables portable building analytics. The core of Energon is a new data organization for building as well as tools that can effectively manage building data and support building analytics development. More specifically, we propose a new "logic partition" of data resources in buildings, and this abstraction universally applies to all buildings. We develop a declarative query language accordingly to f ind data resources in this new logic view with high-level queries, thus substantially reducing development efforts. We also develop a query engine with automatic data extraction by traversing building ontology that widely exists in buildings. In this way, Energon enables mapping of analytics requirements to building resources in a building-agnostic manner. Using four types of real-world building analytics, we demonstrate the use of Energon and its effectiveness in reducing development efforts.