Harnessing the power of LLMs for normative reasoning in MASs

• URL: http://arxiv.org/abs/2403.16524v2
• Date: Mon, 14 Oct 2024 02:20:10 GMT
• Title: Harnessing the power of LLMs for normative reasoning in MASs
• Authors: Bastin Tony Roy Savarimuthu, Surangika Ranathunga, Stephen Cranefield,
• Abstract summary: Large Language Models (LLMs) offer rich and expressive vocabulary for norms. LLMs can perform a range of tasks such as norm discovery, normative reasoning and decision-making. This paper aims to foster collaboration between MAS, NLP and LLM researchers in order to advance the field of normative agents.
• Score: 3.1796285054362605
• License:
• Abstract: Software agents, both human and computational, do not exist in isolation and often need to collaborate or coordinate with others to achieve their goals. In human society, social mechanisms such as norms ensure efficient functioning, and these techniques have been adopted by researchers in multi-agent systems (MAS) to create socially aware agents. However, traditional techniques have limitations, such as operating in limited environments often using brittle symbolic reasoning. The advent of Large Language Models (LLMs) offers a promising solution, providing a rich and expressive vocabulary for norms and enabling norm-capable agents that can perform a range of tasks such as norm discovery, normative reasoning and decision-making. This paper examines the potential of LLM-based agents to acquire normative capabilities, drawing on recent Natural Language Processing (NLP) and LLM research. We present our vision for creating normative LLM agents. In particular, we discuss how the recently proposed "LLM agent" approaches can be extended to implement such normative LLM agents. We also highlight challenges in this emerging field. This paper thus aims to foster collaboration between MAS, NLP and LLM researchers in order to advance the field of normative agents.

Related papers

• WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
  Large language models (LLMs) can mimic human-like intelligence. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
  arXiv  Detail & Related papers  (2024-07-07T07:15:49Z)
• Large Language Models as Instruments of Power: New Regimes of Autonomous Manipulation and Control [0.0]
  Large language models (LLMs) can reproduce a wide variety of rhetorical styles and generate text that expresses a broad spectrum of sentiments. We consider a set of underestimated societal harms made possible by the rapid and largely unregulated adoption of LLMs.
  arXiv  Detail & Related papers  (2024-05-06T19:52:57Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Large Multimodal Agents: A Survey [78.81459893884737]
  Large language models (LLMs) have achieved superior performance in powering text-based AI agents. There is an emerging research trend focused on extending these LLM-powered AI agents into the multimodal domain. This review aims to provide valuable insights and guidelines for future research in this rapidly evolving field.
  arXiv  Detail & Related papers  (2024-02-23T06:04:23Z)
• Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects [32.91556128291915]
  This paper surveys current research to provide an in-depth overview of intelligent agents within single and multi-agent systems. It covers their definitions, research frameworks, and foundational components such as their composition, cognitive and planning methods, tool utilization, and responses to environmental feedback. We conclude by envisioning prospects for LLM-based agents, considering the evolving landscape of AI and natural language processing.
  arXiv  Detail & Related papers  (2024-01-07T09:08:24Z)
• Formally Specifying the High-Level Behavior of LLM-Based Agents [24.645319505305316]
  LLMs have emerged as promising tools for solving challenging problems without the need for task-specific finetuned models. Currently, the design and implementation of such agents is ad hoc, as the wide variety of tasks that LLM-based agents may be applied to naturally means there can be no one-size-fits-all approach to agent design. We propose a minimalistic generation framework that simplifies the process of building agents.
  arXiv  Detail & Related papers  (2023-10-12T17:24:15Z)
• LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving [87.1164964709168]
  This work employs Large Language Models (LLMs) as a decision-making component for complex autonomous driving scenarios. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination.
  arXiv  Detail & Related papers  (2023-10-04T17:59:49Z)
• Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View [60.80731090755224]
  This paper probes the collaboration mechanisms among contemporary NLP systems by practical experiments with theoretical insights. We fabricate four unique societies' comprised of LLM agents, where each agent is characterized by a specific trait' (easy-going or overconfident) and engages in collaboration with a distinct thinking pattern' (debate or reflection) Our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring social psychology theories.
  arXiv  Detail & Related papers  (2023-10-03T15:05:52Z)
• AgentBench: Evaluating LLMs as Agents [88.45506148281379]
  Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. We present AgentBench, a benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities.
  arXiv  Detail & Related papers  (2023-08-07T16:08:11Z)
• Auditing large language models: a three-layered approach [0.0]
  Large language models (LLMs) represent a major advance in artificial intelligence (AI) research. LLMs are also coupled with significant ethical and social challenges. Previous research has pointed towards auditing as a promising governance mechanism.
  arXiv  Detail & Related papers  (2023-02-16T18:55:21Z)

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.