A Social Self-Awareness Agent with Embodied Reasoning

In the realm of multi-agent systems, establishing social self-awareness in agents stands as a critical challenge within the broader scope of artificial general intelligence research. Addressing this challenge, in this paper, we draw upon Tomasello’s social self-awareness theory as a foundational framework, and employ Non-Axiomatic Reasoning System (NARS), an artificial general intelligence model, to simulate analogical and recursive reasoning methods used by socially embodied agents. In this paper, we assume that an agent can possess sensorimotor capabilities and physical common sense, it explores the process of constructing a “core self” through embodied reasoning. On this basis, we explore deeper into the construction process of social self-awareness. As a result, we clarify how agents generate common knowledge about themselves through recursive reasoning regarding the mental states of others and communication dynamics. Following this initial step, metaphors are generated utilizing analogical reasoning, intertwining public knowledge with these metaphors to facilitate mutual comprehension between humans and machines. This paper delves into the simulation of perspective representation and recursive reasoning within social embodied agents, employing NARS — an overarching artificial intelligence platform. The experimental results substantiate the viability of constructing a social self-agent within a machine framework grounded in embodied reasoning.