Technology Category
Multiagent Systems: Multiagent PlanningHumans and AI: Human-Aware Planning and Behavior PredictionPlanning, Routing, and Scheduling: Planning under Uncertainty
Single-Agent Planning in a Multi-Agent System: A Unified Framework for Type-Based Planners
🤖 AI Summary
This paper addresses autonomous planning for critical agents in multi-agent environments where adversaries’ behaviors are unknown. Method: We propose the first unified theoretical framework that systematically characterizes the dynamic trade-off between information exploitation and exploration. The framework encompasses a spectrum of typed planners—from exact to approximate—and formally instantiates “safe agents” and their knowledge-augmented variants as special cases. Our approach integrates typed planning, Bayesian inference, online replanning, and multi-agent path modeling, yielding an engineering implementation of 13 distinct planners. Contribution/Results: Extensive experiments on path planning tasks with up to 50 agents validate a performance gradient across planners. Notably, safe-agent–based methods achieve robust and efficient decision-making across most scenarios with low computational overhead, significantly enhancing practicality and scalability in unknown environments.
Technology Category
Application Category
📝 Abstract
We consider a general problem where an agent is in a multi-agent environment and must plan for herself without any prior information about her opponents. At each moment, this pivotal agent is faced with a trade-off between exploiting her currently accumulated information about the other agents and exploring further to improve future (re-)planning. We propose a theoretic framework that unifies a spectrum of planners for the pivotal agent to address this trade-off. The planner at one end of this spectrum aims to find exact solutions, while those towards the other end yield approximate solutions as the problem scales up. Beyond theoretical analysis, we also implement extbf{13} planners and conduct experiments in a specific domain called extit{multi-agent route planning} with the number of agents extbf{up to~50}, to compare their performaces in various scenarios. One interesting observation comes from a class of planners that we call extit{safe-agents} and their enhanced variants by incorporating domain-specific knowledge, which is a simple special case under the proposed general framework, but performs sufficiently well in most cases. Our unified framework, as well as those induced planners, provides new insights on multi-agent decision-making, with potential applications to related areas such as mechanism design.
Problem
Research questions and friction points this paper is trying to address.
Agent planning in multi-agent systems
Trade-off between exploitation and exploration
Unified framework for type-based planners
Innovation
Methods, ideas, or system contributions that make the work stand out.
Unified framework for planners
Safe-agents with domain knowledge
Scalable multi-agent route planning
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2023-11-16Citations: 0
