language strategy
Simulation of Language Evolution under Regulated Social Media Platforms: A Synergistic Approach of Large Language Models and Genetic Algorithms
Cai, Jinyu, Ishimizu, Yusei, Zhang, Mingyue, Li, Munan, Li, Jialong, Tei, Kenji
Social media platforms frequently impose restrictive policies to moderate user content, prompting the emergence of creative evasion language strategies. This paper presents a multi-agent framework based on Large Language Models (LLMs) to simulate the iterative evolution of language strategies under regulatory constraints. In this framework, participant agents, as social media users, continuously evolve their language expression, while supervisory agents emulate platform-level regulation by assessing policy violations. To achieve a more faithful simulation, we employ a dual design of language strategies (constraint and expression) to differentiate conflicting goals and utilize an LLM-driven GA (Genetic Algorithm) for the selection, mutation, and crossover of language strategies. The framework is evaluated using two distinct scenarios: an abstract password game and a realistic simulated illegal pet trade scenario. Experimental results demonstrate that as the number of dialogue rounds increases, both the number of uninterrupted dialogue turns and the accuracy of information transmission improve significantly. Furthermore, a user study with 40 participants validates the real-world relevance of the generated dialogues and strategies. Moreover, ablation studies validate the importance of the GA, emphasizing its contribution to long-term adaptability and improved overall results.
- Asia > Japan > Honshū > Kantō > Tokyo Metropolis Prefecture > Tokyo (0.14)
- Asia > China > Liaoning Province > Dalian (0.04)
- Law (1.00)
- Law Enforcement & Public Safety > Crime Prevention & Enforcement (0.46)
- Information Technology > Communications > Social Media (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Large Language Model (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
How Experience of the Body Shapes Language about Space
Steels, Luc L. (Sony Computer Science Laboratory) | Spranger, Michael (Sony Computer Science Laboratory Paris)
Open-ended language communication remains an enormous challenge for autonomous robots. This paper argues that the notion of a language strategy is the appropriate vehicle for addressing this challenge. A language strategy packages all the procedures that are necessary for playing a language game. We present a specific example of a language strategy for playing an Action Game in which one robot asks another robot to take on a body posture (such as stand or sit), and show how it effectively allows a population of agents to self-organise a perceptually grounded ontology and a lexicon from scratch, without any human intervention. Next, we show how a new language strategy can arise by exaptation from an existing one, concretely, how the body posture strategy can be exapted to a strategy for playing language games about the spatial position of objects (as in "the bottle stands on the table").
- North America > United States > New York (0.04)
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
- Europe > Netherlands > North Holland > Amsterdam (0.04)
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- Information Technology > Artificial Intelligence > Robots (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.47)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.46)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Ontologies (0.36)