Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games

• URL: http://arxiv.org/abs/2402.13399v2
• Date: Thu, 22 Feb 2024 15:46:21 GMT
• Title: Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games
• Authors: Ninell Oldenburg and Tan Zhi-Xuan
• Abstract summary: We build learning agents that cooperate flexibly with the human institutions they are embedded in. By assuming shared norms, a newly introduced agent can infer the norms of an existing population from observations of compliance and violation. Since agents can bootstrap common knowledge of the norms, this leads the norms to be widely adhered to, enabling new entrants to rapidly learn those norms.
• Score: 2.307051163951559
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A universal feature of human societies is the adoption of systems of rules and norms in the service of cooperative ends. How can we build learning agents that do the same, so that they may flexibly cooperate with the human institutions they are embedded in? We hypothesize that agents can achieve this by assuming there exists a shared set of norms that most others comply with while pursuing their individual desires, even if they do not know the exact content of those norms. By assuming shared norms, a newly introduced agent can infer the norms of an existing population from observations of compliance and violation. Furthermore, groups of agents can converge to a shared set of norms, even if they initially diverge in their beliefs about what the norms are. This in turn enables the stability of the normative system: since agents can bootstrap common knowledge of the norms, this leads the norms to be widely adhered to, enabling new entrants to rapidly learn those norms. We formalize this framework in the context of Markov games and demonstrate its operation in a multi-agent environment via approximately Bayesian rule induction of obligative and prohibitive norms. Using our approach, agents are able to rapidly learn and sustain a variety of cooperative institutions, including resource management norms and compensation for pro-social labor, promoting collective welfare while still allowing agents to act in their own interests.

Related papers

• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• Agent Alignment in Evolving Social Norms [65.45423591744434]
  We propose an evolutionary framework for agent evolution and alignment, named EvolutionaryAgent. In an environment where social norms continuously evolve, agents better adapted to the current social norms will have a higher probability of survival and proliferation. We show that EvolutionaryAgent can align progressively better with the evolving social norms while maintaining its proficiency in general tasks.
  arXiv  Detail & Related papers  (2024-01-09T15:44:44Z)
• Value Engineering for Autonomous Agents [3.6130723421895947]
  Previous approaches have treated values as labels associated with some actions or states of the world, rather than as integral components of agent reasoning. We propose a new AMA paradigm grounded in moral and social psychology, where values are instilled into agents as context-dependent goals. We argue that this type of normative reasoning, where agents are endowed with an understanding of norms' moral implications, leads to value-awareness in autonomous agents.
  arXiv  Detail & Related papers  (2023-02-17T08:52:15Z)
• NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly [61.77957329364812]
  We introduce a framework for addressing the novel task of conversation-grounded multi-lingual, multi-cultural norm discovery. NormSAGE elicits knowledge about norms through directed questions representing the norm discovery task and conversation context. It further addresses the risk of language model hallucination with a self-verification mechanism ensuring that the norms discovered are correct.
  arXiv  Detail & Related papers  (2022-10-16T18:30:05Z)
• Socially Intelligent Genetic Agents for the Emergence of Explicit Norms [0.0]
  We address the emergence of explicit norms by developing agents who provide and reason about explanations for norm violations. These agents use a genetic algorithm to produce norms and reinforcement learning to learn the values of these norms. We find that applying explanations leads to norms that provide better cohesion and goal satisfaction for the agents.
  arXiv  Detail & Related papers  (2022-08-07T18:48:48Z)
• Aligning to Social Norms and Values in Interactive Narratives [89.82264844526333]
  We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games. We introduce the GALAD agent that uses the social commonsense knowledge present in specially trained language models to contextually restrict its action space to only those actions that are aligned with socially beneficial values.
  arXiv  Detail & Related papers  (2022-05-04T09:54:33Z)
• Interpretable Reinforcement Learning with Multilevel Subgoal Discovery [77.34726150561087]
  We propose a novel Reinforcement Learning model for discrete environments. In the model, an agent learns information about environment in the form of probabilistic rules. No reward function is required for learning; an agent only needs to be given a primary goal to achieve.
  arXiv  Detail & Related papers  (2022-02-15T14:04:44Z)
• Normative Disagreement as a Challenge for Cooperative AI [56.34005280792013]
  We argue that typical cooperation-inducing learning algorithms fail to cooperate in bargaining problems. We develop a class of norm-adaptive policies and show in experiments that these significantly increase cooperation.
  arXiv  Detail & Related papers  (2021-11-27T11:37:42Z)
• Norm Identification through Plan Recognition [22.387008072671005]
  Societal rules aim to provide a degree of behavioural stability to multi-agent societies. Many implementations of normative systems assume various combinations of the following assumptions. We develop a norm identification mechanism that uses a combination of parsing-based plan recognition and Hierarchical Task Network (HTN) planning mechanisms.
  arXiv  Detail & Related papers  (2020-10-06T11:18:52Z)
• A Norm Emergence Framework for Normative MAS -- Position Paper [0.90238471756546]
  We propose a framework for the emergence of norms within a normative multiagent system. We make the case that, similarly, a norm has emerged in a normative MAS when a percentage of agents adopt the norm. We put forward a framework for the emergence of norms within a normative MAS, while special-purpose synthesizer agents formulate new norms or revisions in response to these requests.
  arXiv  Detail & Related papers  (2020-04-06T11:42:01Z)

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.