These are exciting times for computational sciences with the digital revolution permeating a variety of areas and radically transforming business, science, and our daily lives. The Internet and the World Wide Web, GPS, satellite communications, remote sensing, and smartphones are dramatically accelerating the pace of discovery, engendering globally connected networks of people and devices. The rise of practically relevant artificial intelligence (AI) is also playing an increasing part in this revolution, fostering e-commerce, social networks, personalized medicine, IBM Watson and AlphaGo, self-driving cars, and other groundbreaking transformations. Unfortunately, humanity is also facing tremendous challenges. Nearly a billion people still live below the international poverty line and human activities and climate change are threatening our planet and the livelihood of current and future generations. Moreover, the impact of computing and information technology has been uneven, mainly benefiting profitable sectors, with fewer societal and environmental benefits, further exacerbating inequalities and the destruction of our planet. Our vision is that computer scientists can and should play a key role in helping address societal and environmental challenges in pursuit of a sustainable future, while also advancing computer science as a discipline. For over a decade, we have been deeply engaged in computational research to address societal and environmental challenges, while nurturing the new field of Computational Sustainability.

Energy Star was launched in 1992 by the US Environmental Protection Agency as a voluntary labelling programme recognising the value of energy-efficiency in a broad range of computer-related products, from personal computers to air-conditioning systems. The programme's major success was the widespread adoption of the energy-saving "sleep mode" in consumer electronic devices. Energy Star's innovative breakthrough represents an important platform from which today's concept of computational sustainability was launched. Computational sustainability is defined as a field of interdisciplinary research that attempts to optimise societal, economic and environmental resources using advanced decision-making algorithms supported by the ever-increasing processing power of today's evolving computer systems. Computational sustainability's key goals include the development of computational models, methods and tools to assist in the management of the delicate balance between environmental, economic and societal needs. Advancements in AI and HCI have enabled combinations of robots and humans to carry out critical functions in the most hostile of environments.

This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the special issue articles that appear in this issue and the previous issue of AI Magazine. The emerging interdisciplinary field of computational sustainability (Gomes 2009) draws techniques from computer science, information science, mathematics, statistics, operations research, and related disciplines to help balance environmental and socioeconomic needs for sustainable development. Artificial intelligence (AI) techniques play a key role in computational sustainability research, enabling the solution of sustainability problems that involve modeling or decision making in dynamic and uncertain environments. Since 2011, the main AAAI conference has included a special track on computational sustainability, encouraging AI research in this area and broader participation of sustainability researchers in the AAAI community. Sustainable solutions must balance between environmental, societal, and economic demands (United Nations General Assembly 2005).

In the United States there is an operational network of weather radars providing freely accessible data for monitoring meteorological phenomena in the atmosphere. Individual radars are sensitive enough to detect birds, and can provide insight into migratory behaviors of birds at scales that are not possible using other sensors. Archived data from the WSR-88D network of U.S. weather radars hold valuable and detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We describe recent work on an AI system to quantify bird migration using radar data, which is part of the larger BirdCast project to model and forecast bird migration at large scales using radar, weather, and citizen science data.

Such problems are generally notoriously difficult. In this article, we review the recently discovered notion of adaptive submodularity, an intuitive diminishing returns condition that generalizes the classical notion of submodular set functions to sequential decision problems. Problems exhibiting the adaptive submodularity property can be efficiently and provably nearoptimally solved using simple myopic policies. We illustrate this concept in several case studies of interest in computational sustainability: First, we demonstrate how it can be used to efficiently plan for resolving uncertainty in adaptive management scenarios. Then, we show how it applies to dynamic conservation planning for protecting endangered species, a case study carried out in collaboration with the U.S. Geological Survey and the U.S. Fish and Wildlife Service.

This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the next two special issue articles that will appear in AI Magazine. The emerging interdisciplinary field of computational sustainability (Gomes 2009) draws techniques from computer science, information science, mathematics, statistics, operations research, and related disciplines to help balance environmental and socioeconomic needs for sustainable development. Artificial intelligence (AI) techniques play a key role in computational sustainability research, enabling the solution of sustainability problems that involve modeling or decision making in dynamic and uncertain environments. Since 2011, the main AAAI conference has included a special track on computational sustainability, encouraging AI research in this area and broader participation of sustainability researchers in the AAAI community. Sustainable solutions must balance between environmental, societal, and economic demands (United Nations General Assembly 2005).

Artificial Intelligence (AI) does more than make our technology smarter, it also protects the planet. Consider the work of researchers in the field of'Computational Sustainability' – a field of AI research making us better stewards of life on Earth. Despite being a relatively new research field, Computational Sustainability has already helped fight wildlife poaching, reduce greenhouse gas emissions, understand poverty, manage wildlife populations, and protect biodiversity. Each of these contributions address one of the United Nations Sustainable Development Goals (SDGs). The collected progress of AI is addressing all SDGs, but I will highlight three specific cases.

This paper and summary talk broadly survey computational sustainability research. Rather than a detailed treatment of the research projects in the area, which is beyond the scope of the paper and talk, the paper includes a meta-survey, pointing to edited collections and overviews in the literature for the interested reader. Computational sustainability research has been broadly characterized by AI methods employed, sustainability areas addressed, and contributions made to (typically, human) decision-making. The paper addresses these characterizations as well, which will facilitate a deeper synthesis later, to include the potential for developing sophisticated and holistic AI decision-making and advisory agents.

When you think of artificial intelligence, the first image that likely comes to mind is one of sentient robots that walk, talk and emote like humans. But a different kind of AI is becoming prevalent in nearly all of the sciences. It's known as machine learning, and it revolves around enlisting computers in the task of sorting through the massive amounts of data that modern technology has allowed us to generate (a.k.a. One place machine learning is turning out to be the most beneficial is in the environmental sciences, which have generated huge amounts of information from monitoring Earth's various systems -- underground aquifers, the warming climate or animal migration, for example. A slew of projects have been popping up in this relatively new field, computational sustainability, that combine data gathered about the environment with a computer's ability to discover trends and make predictions about the future of our planet.