Cooperating with machines

By Jacob William Crandall, Mayada Oudah, Tennom Chenlinangjia, Fatimah Ishowo-Oloko, Sherief Abdallah, Jean‐François Bonnefon, Manuel Cebrian, Azim Shariff, Michael A. Goodrich, Iyad Rahwan. 2017

251 citations recorded by the source corpus.

Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human–machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human–machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms. Artificial intelligence is now superior to humans in many fully competitive games, such as Chess, Go, and Poker. Here the authors develop a machine-learning algorithm that can cooperate effectively with humans when cooperation is beneficial but nontrivial, something humans are remarkably good at.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Continuous Coordination As a Realistic Scenario for Lifelong Learning (2021, background): “Cooperative settings are more reflective of real-world scenarios such as autonomous driving and are crucial for human-AI collaboration (Crandall et al., 2018).”
Emergent Communication through Negotiation (2018, background): “Recent encouraging results by Crandall et al. (2018) show that communication can help agents cooperate.”
Prosocial learning agents solve generalized Stag Hunts better than selfish ones (2017, background): “Understanding how to design human-machine hybrid systems in ways that properly account for human psychology (Crandall et al. 2017; Kleiman-Weiner et al. 2016; Rand et al. 2014; Hauser et al. 2014; Ouss and Peysakhovich 2015; Arechar et al. 2016; Mao et al. 2017) and also human learning strategies…”
Prompt Engineering for Text-Based Generative Art (2022, background): “The latter can be viewed from many different perspectives, such as human-centered AI [32], human-AI partnerships [29], and human-AI cooperation [5].”
Maintaining cooperation in complex social dilemmas using deep reinforcement learning (2017, background): “There is a growing literature on hybrid systems which include both human and artificial agents (Crandall et al., 2017; Shirado & Christakis, 2017).”
Human Decision Making with Machine Assistance (2019, background): “Crandall et al. [20] and Dimitrakakis et al. [23] study algorithms that are able to communicate their strategy to humans, and demonstrate their benefits empirically [20] and theoretically [23].”
Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI (2021, background): “…of hybrid augmented intelligence help achieve collaborative decision-making in complex problems, thereby gaining superior excellent results that cannot be achieved separately (e.g., Carter & Nielsen, 2017; Crandall, 2018; Dellermann, Calma, et al., 2019; Dellermann, Ebel, et al., 2019).”