Informational Design of Dynamic Multi-Agent System

• URL: http://arxiv.org/abs/2105.03052v1
• Date: Fri, 7 May 2021 03:46:14 GMT
• Title: Informational Design of Dynamic Multi-Agent System
• Authors: Tao Zhang and Quanyan Zhu
• Abstract summary: We study how the craft of payoffrelevant environmental signals solely can influence the behaviors of intelligent agents. An obedient principle is established which states that it is without loss of generality to focus on the direct information design. A framework is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal.
• Score: 32.37168850559519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work considers a novel information design problem and studies how the craft of payoff-relevant environmental signals solely can influence the behaviors of intelligent agents. The agents' strategic interactions are captured by an incomplete-information Markov game, in which each agent first selects one environmental signal from multiple signal sources as additional payoff-relevant information and then takes an action. There is a rational information designer (principal) who possesses one signal source and aims to control the equilibrium behaviors of the agents by designing the information structure of her signals sent to the agents. An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the principal, such that the design process avoids the predictions of the agents' strategic selection behaviors. Based on the obedient principle, we introduce the design protocol given a goal of the principal referred to as obedient implementability (OIL) and study a Myersonian information design that characterizes the OIL in a class of obedient sequential Markov perfect Bayesian equilibria (O-SMPBE). A framework is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, makes sure that the agents' policy profile of taking actions is the policy component of an O-SMPBE, and the principal's goal is achieved. The proposed approach can be applied to elicit desired behaviors of multi-agent systems in competing as well as cooperating settings and be extended to heterogeneous stochastic games in the complete- and the incomplete-information environments.

Related papers

• The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey [0.0]
  This paper examines the recent advancements in AI agent implementations. It focuses on their ability to achieve complex goals that require enhanced reasoning, planning, and tool execution capabilities.
  arXiv  Detail & Related papers  (2024-04-17T17:32:41Z)
• Pure Exploration under Mediators' Feedback [63.56002444692792]
  Multi-armed bandits are a sequential-decision-making framework, where, at each interaction step, the learner selects an arm and observes a reward. We consider the scenario in which the learner has access to a set of mediators, each of which selects the arms on the agent's behalf according to a and possibly unknown policy. We propose a sequential decision-making strategy for discovering the best arm under the assumption that the mediators' policies are known to the learner.
  arXiv  Detail & Related papers  (2023-08-29T18:18:21Z)
• Information Design in Multi-Agent Reinforcement Learning [61.140924904755266]
  Reinforcement learning (RL) is inspired by the way human infants and animals learn from the environment. Research in computational economics distills two ways to influence others directly: by providing tangible goods (mechanism design) and by providing information (information design)
  arXiv  Detail & Related papers  (2023-05-08T07:52:15Z)
• Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments [55.41685740015095]
  We study offline reinforcement learning under a novel model called strategic MDP. We propose a novel algorithm, Pessimistic policy Learning with Algorithmic iNstruments (PLAN)
  arXiv  Detail & Related papers  (2022-08-23T15:32:44Z)
• Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel [81.39444892747512]
  In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another. We propose an alternative approach whereby agents communicate through an intelligent facilitator that learns to sift through and interpret signals provided by all agents to improve the agents' collective performance.
  arXiv  Detail & Related papers  (2022-05-21T14:11:33Z)
• On the Equilibrium Elicitation of Markov Games Through Information Design [32.37168850559519]
  We study how the craft of payoffrelevant environmental signals solely can influence the behaviors of intelligent agents. An obedient principle is established which states that it is without loss of generality to focus on the direct information design. A new framework for information design is proposed based on an approach of maximizing the optimal slack variables.
  arXiv  Detail & Related papers  (2021-02-14T13:30:06Z)
• "I Don't Think So": Disagreement-Based Policy Summaries for Comparing Agents [2.6270468656705765]
  We propose a novel method for generating contrastive summaries that highlight the differences between agent's policies. Our results show that the novel disagreement-based summaries lead to improved user performance compared to summaries generated using HIGHLIGHTS.
  arXiv  Detail & Related papers  (2021-02-05T09:09:00Z)
• Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous [66.6895109554163]
  Underlying the human ability to align goals with other agents is their ability to predict the intentions of others and actively update their own plans. We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decentralized multiagent rendezvous.
  arXiv  Detail & Related papers  (2020-03-15T19:49:20Z)

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.