Artificial Intelligence for Computational Sustainability: A Lab Companion

Preamble
{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) {| style="width:100%;"
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0. /Preface/ for educators and learners

1. /Introduction/ to Computational Sustainability

AI Chapters
2. /State Space Search/

3. /Constraint-Based Reasoning and Optimization/

4. /Knowledge Representation/

5. /Reasoning Under Uncertainty/

6. /Machine Learning for Prediction/

7. /Deterministic Planning and Problem Solving/

8. /Planning Under Uncertainty/

9. /Machine Learning for Planning and Problem Solving/

10. /Multi-Agent Systems/

Sustainability Chapters
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/
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Additional Resources
/List of Computational Sustainability Courses/ Alan Mackworth's List of Resources