Decentralised Emergence of Robust and Adaptive Linguistic Conventions in Populations of Autonomous Agents Grounded in Continuous Worlds

Abstract: This paper introduces a methodology through which a population of autonomous agents can establish a linguistic convention that enables them to refer to arbitrary entities that they observe in their environment. The linguistic convention emerges in a decentralised manner through local communicative interactions between pairs of agents drawn from the population. The convention consists of symbolic labels (word forms) associated to concept representations (word meanings) that are grounded in a continuous feature space. The concept representations of each agent are individually constructed yet compatible on a communicative level. Through a range of experiments, we show (i) that the methodology enables a population to converge on a communicatively effective, coherent and human-interpretable linguistic convention, (ii) that it is naturally robust against sensor defects in individual agents, (iii) that it can effectively deal with noisy observations, uncalibrated sensors and heteromorphic populations, (iv) that the method is adequate for continual learning, and (v) that the convention self-adapts to changes in the environment and communicative needs of the agents.

• The paper introduces a decentralized methodology that enables autonomous agents to invent and align effective linguistic conventions without any prior inventory.
• It employs a language game paradigm with context-driven interactions to evolve adaptive, robust, and coherent communication systems across varied datasets.
• Experimental results demonstrate high communicative success under sensor anomalies and environmental variability, highlighting broad applications in AI.

Decentralised Emergence of Robust and Adaptive Linguistic Conventions in Autonomous Agent Populations

Introduction

The exploration of linguistics within artificial intelligence has taken a significant leap with a novel methodology facilitating the decentralised emergence of linguistic conventions amongst autonomous agents grounded in continuous worlds. This research, conducted by Jérôme Botoko Ekila et al., moves beyond the traditional boundaries set by pre-existing concepts and encapsulated environments, allowing for the evolution of languages that are not only communicatively effective and coherent but also robust and adaptable to the agents' communicative needs and environmental changes.

Methodological Framework

The foundation of this methodology is deeply rooted in the language game paradigm, enhancing the way agents invent, adopt, and align concept representations within a multi-agent system. Each agent, devoid of any initial linguistic inventory, engages in situated, task-oriented communicative interactions, enabling the gradual construction of individual yet communicably compatible linguistic inventories. The framework delineates a structured process involving context selection, agent and role selection, topic selection, and subsequent alignment processes reinforcing successful communication and penalizing failures. Through this paradigm, languages that are semantically rich and flexible emerge, guided by the evolutionary dynamics inherent to human linguistic development.

Experimental Validation

The robustness, flexibility, and adaptability of the emerging linguistic conventions were methodically validated across diverse datasets representing visual scenes, physicochemical properties, and principal components of financial transactions. Notably, the populations of agents attained a remarkable degree of communicative success and coherence, irrespective of the dataset. Additional experiments addressed the methodology's responsiveness to challenges such as compositional generalisability, heteromorphic populations, sensor defects, and varying perceptions due to noise or lack of calibration, showcasing exceptional resilience and adaptability.

Implications and Speculations on Future Developments

This research significantly advances the computational modelling of language emergence, presenting a versatile methodology applicable across a wide array of domains. The emergence of communicatively effective, coherent, and human-interpretable linguistic conventions in decentralized systems underscores the potential for creating more dynamic and adaptable communication systems within artificial intelligence. Looking forward, this could pave the way for advancements in how machines understand and interact with their environment and with each other, potentially leading to more nuanced and naturalistic human-machine interfaces. Moreover, the methodology's robustness against environmental variability and sensor anomalies suggests promising applications in developing autonomous systems capable of adapting their communicative strategies to unforeseen challenges or changes in their operational domains.

Concluding Remarks

The study marks a significant milestone in the endeavour to model the emergence and evolution of human-like languages in artificial environments. By transcending the limitations of prior methodologies, it offers a comprehensive framework for understanding and facilitating the development of linguistic conventions among autonomous agents. The demonstrated capabilities of this approach not only enrich our understanding of the dynamics of language evolution but also open new avenues for exploring the interaction between artificial intelligence and complex linguistic constructs in continuously changing worlds.