General Automatic Solution Generation of Social Problems

• URL: http://arxiv.org/abs/2401.13945v1
• Date: Thu, 25 Jan 2024 05:00:46 GMT
• Title: General Automatic Solution Generation of Social Problems
• Authors: Tong Niu, Haoyu Huang, Yu Du, Weihao Zhang, Luping Shi, Rong Zhao
• Abstract summary: We report an automatic social operating system (ASOS) designed for general social solution generation. ASOS is built upon agent-based models, enabling both global and local analyses and regulations of social problems. By generating a new trading role, ASOS can adeptly discern precarious market conditions and make front-running interventions for non-profit purposes.
• Score: 13.57217244470763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given the escalating intricacy and multifaceted nature of contemporary social systems, manually generating solutions to address pertinent social issues has become a formidable task. In response to this challenge, the rapid development of artificial intelligence has spurred the exploration of computational methodologies aimed at automatically generating solutions. However, current methods for auto-generation of solutions mainly concentrate on local social regulations that pertain to specific scenarios. Here, we report an automatic social operating system (ASOS) designed for general social solution generation, which is built upon agent-based models, enabling both global and local analyses and regulations of social problems across spatial and temporal dimensions. ASOS adopts a hypergraph with extensible social semantics for a comprehensive and structured representation of social dynamics. It also incorporates a generalized protocol for standardized hypergraph operations and a symbolic hybrid framework that delivers interpretable solutions, yielding a balance between regulatory efficacy and function viability. To demonstrate the effectiveness of ASOS, we apply it to the domain of averting extreme events within international oil futures markets. By generating a new trading role supplemented by new mechanisms, ASOS can adeptly discern precarious market conditions and make front-running interventions for non-profit purposes. This study demonstrates that ASOS provides an efficient and systematic approach for generating solutions for enhancing our society.

Related papers

• GSON: A Group-based Social Navigation Framework with Large Multimodal Model [9.94576166903495]
  We present a group-based social navigation framework GSON to enable mobile robots to perceive and exploit the social group of their surroundings. For perception, we apply visual prompting techniques to zero-shot extract the social relationship among pedestrians. For planning, we adopt a social structure-based mid-level planner as a bridge between global path planning and local motion planning.
  arXiv  Detail & Related papers  (2024-09-26T17:27:15Z)
• Selective Exploration and Information Gathering in Search and Rescue Using Hierarchical Learning Guided by Natural Language Input [5.522800137785975]
  We introduce a system that integrates social interaction via large language models (LLMs) with a hierarchical reinforcement learning (HRL) framework. The proposed system is designed to translate verbal inputs from human stakeholders into actionable RL insights and adjust its search strategy. By leveraging human-provided information through LLMs and structuring task execution through HRL, our approach significantly improves the agent's learning efficiency and decision-making process in environments characterised by long horizons and sparse rewards.
  arXiv  Detail & Related papers  (2024-09-20T12:27:47Z)
• DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism [55.45581907514175]
  This paper proposes a personalized federated learning framework with a dual aggregation mechanism for social event detection, namely DAMe. We introduce a global aggregation strategy to provide clients with maximum external knowledge of their preferences. In addition, we incorporate a global-local event-centric constraint to prevent local overfitting and client-drift''
  arXiv  Detail & Related papers  (2024-09-01T04:56:41Z)
• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems [62.252355444948904]
  This paper presents the findings of a literature review on the integration of edge intelligence (EI) and socialized learning (SL) SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents. We elaborate on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses.
  arXiv  Detail & Related papers  (2024-04-20T11:07:29Z)
• HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments [93.94020724735199]
  HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
  arXiv  Detail & Related papers  (2024-01-23T18:59:43Z)
• Simulating Public Administration Crisis: A Novel Generative Agent-Based Simulation System to Lower Technology Barriers in Social Science Research [0.0]
  This article proposes a social simulation paradigm based on the GPT-3.5 large language model. It involves constructing Generative Agents that emulate human cognition, memory, and decision-making frameworks. Agents exhibit personalized customization, and public events are seamlessly incorporated through natural language processing.
  arXiv  Detail & Related papers  (2023-11-12T20:48:01Z)
• SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents [107.4138224020773]
  We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and humans. In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals. We find that GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills.
  arXiv  Detail & Related papers  (2023-10-18T02:27:01Z)
• A technological framework for scalable ground-up formation of Circular Societies [0.0]
  Circular Economy (CE) is regarded as a solution to the environmental crisis. mainstream CE measures skirt around challenging the ethos of ever-increasing economic growth. Circular Societies (CS) address these concerns by challenging this ethos.
  arXiv  Detail & Related papers  (2023-04-28T15:35:27Z)
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [80.49176924360499]
  We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems. We derive a class of decentralized reinforcement learning algorithms. We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
  arXiv  Detail & Related papers  (2020-07-05T16:41:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.