Artificial Intelligence and the Common Sense of Animals

摘要

•Endowing computers with common sense remains one of the biggest challenges in the field of artificial intelligence (AI).•Most treatments of the topic foreground language, yet an understanding of everyday concepts such as objecthood, containers, obstructions, paths, etc. is arguably: (i) a prerequisite for language, and (ii) evident to some degree in non-human animals.•The recent advent of deep reinforcement learning (RL) in 3D simulated environments allows AI researchers to train and test (virtually) embodied agents in conditions analogous to the life of an animal.•With the right architecture, an RL agent inhabiting a simulated 3D world has the potential to acquire a repertoire of fundamental common sense concepts and principles, given suitable environments, tasks, and curricula.•Experimental protocols from the field of animal cognition can be repurposed for evaluating the extent to which an agent, after training, ‘understands’ a common sense concept or principle, in particular in a transfer setting. The problem of common sense remains a major obstacle to progress in artificial intelligence. Here, we argue that common sense in humans is founded on a set of basic capacities that are possessed by many other animals, capacities pertaining to the understanding of objects, space, and causality. The field of animal cognition has developed numerous experimental protocols for studying these capacities and, thanks to progress in deep reinforcement learning (RL), it is now possible to apply these methods directly to evaluate RL agents in 3D environments. Besides evaluation, the animal cognition literature offers a rich source of behavioural data, which can serve as inspiration for RL tasks and curricula. The problem of common sense remains a major obstacle to progress in artificial intelligence. Here, we argue that common sense in humans is founded on a set of basic capacities that are possessed by many other animals, capacities pertaining to the understanding of objects, space, and causality. The field of animal cognition has developed numerous experimental protocols for studying these capacities and, thanks to progress in deep reinforcement learning (RL), it is now possible to apply these methods directly to evaluate RL agents in 3D environments. Besides evaluation, the animal cognition literature offers a rich source of behavioural data, which can serve as inspiration for RL tasks and curricula. The challenge of endowing computers with common sense has been seen as a major obstacle to achieving the boldest aims of artificial intelligence (AI) since the field’s earliest days [1.McCarthy J. Programs with common sense.in: Proceedings of the Teddington Conference on the Mechanization of Thought Processes. Her Majesty’s Stationary Office, 1959: 75-91Google Scholar] and it remains a significant problem today [2.Garnelo M. et al.Towards deep symbolic reinforcement learning.arXiv. 2016; (1609.05518)Google Scholar, 3.Davis E. Marcus G. Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence.Commun. ACM. 2015; 58: 92-103Crossref Scopus (175) Google Scholar, 4.Lake B.M. et al.Building machines that learn and think like people.Behav. Brain Sci. 2017; 40Crossref Scopus (705) Google Scholar, 5.Marcus G. Davis E. Rebooting AI: Building Artificial Intelligence We Can Trust. Ballantine Books Inc., 2019Google Scholar, 6.Smith B.C. The Promise of Artificial Intelligence: Reckoning and Judgment. MIT Press, 2019Crossref Google Scholar]. There is no universally accepted definition of common sense. However, most authors use language as a touchstone, following the example of [1.McCarthy J. Programs with common sense.in: Proceedings of the Teddington Conference on the Mechanization of Thought Processes. Her Majesty’s Stationary Office, 1959: 75-91Google Scholar], who stated that ‘[a] program has common sense if it automatically deduces for itself a sufficiently wide class of immediate consequences of anything it is told and what it alread