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1. Becoming Good at AI for Good

   https://doi.org/10.1145/3461702.3462599  

   Kshirsagar, Meghana; Robinson, Caleb; Yang, Siyu; Gholami, Shahrzad; Klyuzhin, Ivan; Mukherjee, Sumit; Nasir, Md; Ortiz, Anthony; Oviedo, Felipe; Tanner, Darren; et al (July 2021, Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’21), May 19–21, 2021,)

   null (Ed.)

   AI for good (AI4G) projects involve developing and applying ar- tificial intelligence (AI) based solutions to further goals in areas such as sustainability, health, humanitarian aid, and social justice. Developing and deploying such solutions must be done in collab- oration with partners who are experts in the domain in question and who already have experience in making progress towards such goals. Based on our experiences, we detail the different aspects of this type of collaboration broken down into four high-level cat- egories: communication, data, modeling, and impact, and distill eleven takeaways to guide such projects in the future. We briefly describe two case studies to illustrate how some of these takeaways were applied in practice during our past collaborations. 

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2. Good times for wireless research

   https://doi.org/10.1016/j.comnet.2021.107870  

   Doost-Mohammady, Rahman; Bejarano, Oscar; Sabharwal, Ashutosh (April 2021, Computer Networks)
3. Good Times For Wireless Research

   https://doi.org/10.1145/3411276.3414557  

   Doost-Mohammady, Rahman; Bejarano, Oscar; Sabharwal, Ashutosh (September 2020, ACM WiNTECH 2020)
4. Alternating Good-for-MDPs Automata

   https://doi.org/10.1007/978-3-031-19992-9_19  

   Hahn, Ernst Moritz; Perez, Mateo; Schewe, Sven; Somenzi, Fabio; Trivedi, Ashutosh; Wojtczak, Dominik (October 2022, International Symposium on Automated Technology for Verification and Analysis (ATVA 2022))

   Bouajjani, A.; Holík, L.; Wu, Z. (Ed.)

   When omega-regular objectives were first proposed in model-free reinforcement learning (RL) for controlling MDPs, deterministic Rabin automata were used in an attempt to provide a direct translation from their transitions to scalar values. While these translations failed, it has turned out that it is possible to repair them by using good-for-MDPs (GFM) Buechi automata instead. These are nondeterministic Buechi automata with a restricted type of nondeterminism, albeit not as restricted as in good-for-games automata. Indeed, deterministic Rabin automata have a pretty straightforward translation to such GFM automata, which is bi-linear in the number of states and pairs. Interestingly, the same cannot be said for deterministic Streett automata: a translation to nondeterministic Rabin or Buechi automata comes at an exponential cost, even without requiring the target automaton to be good-for-MDPs. Do we have to pay more than that to obtain a good-for-MDPs automaton? The surprising answer is that we have to pay significantly less when we instead expand the good-for-MDPs property to alternating automata: like the nondeterministic GFM automata obtained from deterministic Rabin automata, the alternating good-for-MDPs automata we produce from deterministic Streett automata are bi-linear in the size of the deterministic automaton and its index. They can therefore be exponentially more succinct than the minimal nondeterministic Buechi automaton. 

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5. Buffers, Especially the Good Kind

   https://doi.org/10.1021/acs.biochem.1c00200  

   Pielak, Gary J. (November 2021, Biochemistry)