SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents

Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Within the Deep Reinforcement Learning (DRL) field, this objective motivated multiple works on embodied language use. However, current approaches focus on language as a communication tool in very simplified and non-diverse social situations: the "naturalness" of language is reduced to the concept of high vocabulary size and variability. In this paper, we argue that aiming towards human-level AI requires a broader set of key social skills: 1) language use in complex and variable social contexts; 2) beyond language, complex embodied communication in multimodal settings within constantly evolving social worlds. We explain how concepts from cognitive sciences could help AI to draw a roadmap towards human-like intelligence, with a focus on its social dimensions. As a first step, we propose to expand current research to a broader set of core social skills. To do this, we present SocialAI, a benchmark to assess the acquisition of social skills of DRL agents using multiple grid-world environments featuring other (scripted) social agents. We then study the limits of a recent SOTA DRL approach when tested on SocialAI and discuss important next steps towards proficient social agents. Videos and code are available at https://sites.google.com/view/socialai.Comment: under review. This paper extends and generalizes work in arXiv:2104.1320

α5β1 Integrin-Mediated Adhesion to Fibronectin Is Required for Axis Elongation and Somitogenesis in Mice

The arginine-glycine-aspartate (RGD) motif in fibronectin (FN) represents the major binding site for α5β1 and αvβ3 integrins. Mice lacking a functional RGD motif in FN (FNRGE/RGE) or α5 integrin develop identical phenotypes characterized by embryonic lethality and a severely shortened posterior trunk with kinked neural tubes. Here we show that the FNRGE/RGE embryos arrest both segmentation and axis elongation. The arrest is evident at about E9.0, corresponding to a stage when gastrulation ceases and the tail bud-derived presomitic mesoderm (PSM) induces α5 integrin expression and assumes axis elongation. At this stage cells of the posterior part of the PSM in wild type embryos are tightly coordinated, express somitic oscillator and cyclic genes required for segmentation, and form a tapered tail bud that extends caudally. In contrast, the posterior PSM cells in FNRGE/RGE embryos lost their tight associations, formed a blunt tail bud unable to extend the body axis, failed to induce the synchronised expression of Notch1 and cyclic genes and cease the formation of new somites. Mechanistically, the interaction of PSM cells with the RGD motif of FN is required for dynamic formation of lamellipodia allowing motility and cell-cell contact formation, as these processes fail when wild type PSM cells are seeded into a FN matrix derived from FNRGE/RGE fibroblasts. Thus, α5β1-mediated adhesion to FN in the PSM regulates the dynamics of membrane protrusions and cell-to-cell communication essential for elongation and segmentation of the body axis

SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents

Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Within the Deep Reinforcement Learning (DRL) field, this objective motivated multiple works on embodied language use. However, current approaches focus on language as a communication tool in very simplified and non-diverse social situations: the "naturalness" of language is reduced to the concept of high vocabulary size and variability. In this paper, we argue that aiming towards human-level AI requires a broader set of key social skills: 1) language use in complex and variable social contexts; 2) beyond language, complex embodied communication in multimodal settings within constantly evolving social worlds. We explain how concepts from cognitive sciences could help AI to draw a roadmap towards human-like intelligence, with a focus on its social dimensions. As a first step, we propose to expand current research to a broader set of core social skills. To do this, we present SocialAI, a benchmark to assess the acquisition of social skills of DRL agents using multiple grid-world environments featuring other (scripted) social agents. We then study the limits of a recent SOTA DRL approach when tested on SocialAI and discuss important next steps towards proficient social agents. Videos and code are available at https://sites.google.com/view/socialai

SocialAI 0.1: Towards a Benchmark to Stimulate Research on Socio-Cognitive Abilities in Deep Reinforcement Learning Agents

Accepted at NAACL ViGIL Workshop 2021International audienceBuilding embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. This problem motivated many research directions on embodied language use. Current approaches focus on language as a communication tool in very simplified and non diverse social situations: the "naturalness" of language is reduced to the concept of high vocabulary size and variability. In this paper, we argue that aiming towards human-level AI requires a broader set of key social skills: 1) language use in complex and variable social contexts; 2) beyond language, complex embodied communication in multimodal settings within constantly evolving social worlds. In this work we explain how concepts from cognitive sciences could help AI to draw a roadmap towards human-like intelligence, with a focus on its social dimensions. We then study the limits of a recent SOTA Deep RL approach when tested on a first grid-world environment from the upcoming SocialAI, a benchmark to assess the social skills of Deep RL agents. Videos and code are available at https://sites.google.com/view/socialai01