25,923 research outputs found
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
On the Role of AI in the Ongoing Paradigm Shift within the Cognitive Sciences
This paper supports the view that the ongoing shift from orthodox to embodied-embedded cognitive science has been significantly influenced by the experimental results generated by AI research. Recently, there has also been a noticeable shift toward enactivism, a paradigm which radicalizes the embodied-embedded approach by placing autonomous agency and lived subjectivity at the heart of cognitive science. Some first steps toward a clarification of the relationship of AI to this further shift are outlined. It is concluded that the success of enactivism in establishing itself as a mainstream cognitive science research program will depend less on progress made in AI research and more on the development of a phenomenological pragmatics
Explore and Explain: Self-supervised Navigation and Recounting
Embodied AI has been recently gaining attention as it aims to foster the development of autonomous and intelligent agents. In this paper, we devise a novel embodied setting in which an agent needs to explore a previously unknown environment while recounting what it sees during the path. In this context, the agent needs to navigate the environment driven by an exploration goal, select proper moments for description, and output natural language descriptions of relevant objects and scenes. Our model integrates a novel self-supervised exploration module with penalty, and a fully-attentive captioning model for explanation. Also, we investigate different policies for selecting proper moments for explanation, driven by information coming from both the environment and the navigation. Experiments are conducted on photorealistic environments from the Matterport3D dataset and investigate the navigation and explanation capabilities of the agent as well as the role of their interactions
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Simple environments fail as illustrations of intelligence: A review of R. Pfeifer and C. Scheier
The field of cognitive science has always supported a variety of modes of research, often polarised into those seeking high-level explanations of intelligence and those seeking low-level, perhaps even neuro-physiological, explanations. Each of these research directions permits, at least in part, a similar methodology based around the construction of detailed computational models, which justify their explanatory claims by matching behavioural data. We are fortunate at this time to witness the culmination of several decades of work from each of these research directions, and hopefully to find within them the basic ideas behind a complete theory of human intelligence. It is in this spirit that Rolf Pfeifer and Christian Scheier have written their book Understanding Intelligence. However, their aim is manifestly not to present an overview of all prior work in this field, but instead to argue forcefully for one particular interpretation â a synthetic approach, based around the explicit construction of autonomous agents. This approach is characterised by the Embodiment Hypothesis, which is presented as a complete framework for investigating intelligence, and exemplified by a number of computational models and robots to illustrate just how the field of cognitive science might develop in the future. We first provide an overview of their book, before describing some of our reservations about its contribution towards an understanding of intelligence
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
PCT and beyond: toward a computational framework for âintelligentâ communicative systems
Recent years have witnessed increasing interest in âintelligentâ autonomous machines such as robots. However, there is a long way to go before autonomous systems reach the level of capabilities required for even the simplest of tasks involving human-robot interaction - especially if it involves communicative behavior such as speech and language. The field of Artificial Intelligence (AI) has made great strides in these areas, and has graduated from high-level rule-based paradigms to embodied architectures whose operations are grounded in real physical environments. What is still missing, however, is an overarching theory of intelligent communicative behavior that informs system-level design decisions. This chapter introduces a framework that extends the principles of Perceptual Control Theory (PCT) toward a remarkably symmetric architecture for a needs-driven communicative agent. It is concluded that, if behavior is the control of perception (the central tenet of PCT), then perception (for communicative agents) is the simulation of behavior
The Contribution of Society to the Construction of Individual Intelligence
It is argued that society is a crucial factor in the construction of individual intelligence. In other words that it is important that intelligence is socially situated in an analogous way to the physical situation of robots. Evidence that this may be the case is taken from developmental linguistics, the social intelligence hypothesis, the complexity of society, the need for self-reflection and autism. The consequences for the development of artificial social agents is briefly considered. Finally some challenges for research into socially situated intelligence are highlighted
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