Artificial Intelligence for Computational Sustainability: A Lab Companion
Preamble
[edit | edit source]{This laboratory companion is designed to introduce students of artificial intelligence (AI) to problems of environmental and societal sustainability, together with projects and problem sets at the intersection of AI and sustainability. The lab text can accompany any primary AI textbook, or can be used independently, though the material in it will typically assume selected knowledge of AI at an undergraduate level. The material in the text is organized primarily around AI topics, and includes explanatory and illustrative material concerning specific sustainability problems, together with projects (of several weeks duration), assignments (of duration on the order of a week) and exercises (on the order of minutes to hours). Indexing into the text is also available through sustainability topics; in addition to describing "open" problems in the sustainability area for which authors feel there is an AI connection, albeit not yet elaborated, this alternative indexing will point to the existing exercises and background material on the sustainability area as it is distributed throughout the AI-centric material. There is also ample cross-referencing between chapters.
Please see how you can contribute: Guide for Contributors
Author: Douglas H. Fisher (AIProf)
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0. Preface for educators and learners 1. Introduction to Computational Sustainability AI Chapters[edit | edit source]3. Constraint-Based Reasoning and Optimization 5. Reasoning Under Uncertainty 6. Machine Learning for Prediction 7. Deterministic Planning and Problem Solving 9. Machine Learning for Planning and Problem Solving Sustainability Chapters[edit | edit source]In these chapters, sustainability problems can be described independent of the AI approaches that might be relevant to address them, thus giving students the opportunity to explore and decide upon the AI approaches that are most appropriate. Background and exercises on given sustainability areas that were found through the computational themes of the AI chapters above, can also be found cross-referenced through the following chapters. 11. Agriculture 12. Behavior and Consumerism 13. Biodiversity and Conservation 14. Climate and Ocean modeling and observation 15. Design, Life-Cycle, and Materials 16. Energy, including Smart Grids 17. Fresh Water Ecosystems and Resources 18. Transportation and Urban Design |
Additional Resources
[edit | edit source]List of Computational Sustainability Courses Alan Mackworth's List of Resources