多智能体系统
This paper introduces a new approach to solving a continuous-time version of the multi-agent path finding problem. The algorithm translates the problem into an extension of the classical Boolean satisfiability problem, satisfiability modulo…
In real-world environments, autonomous agents rely on their egocentric observations. They must learn adaptive strategies to interact with others who possess mixed motivations, discernible only through visible cues. Several Multi-Agent…
This paper examines the spatial coverage optimization problem for multiple sensors in a known convex environment, where the coverage service of each sensor is heterogeneous and anisotropic. We introduce the Stein Coverage algorithm, a…
In multi-agent reinforcement learning (MARL), self-interested agents attempt to establish equilibrium and achieve coordination depending on game structure. However, existing MARL approaches are mostly bound by the simultaneous actions of…
Servizi Elaborazioni Dati SpA is a public company owned by Municipality of L Aquila, it supplies the institution with network services and software applications for distributing services to citizens. The future policy of the company is to…
Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous disciplines, including game theory, economics, social sciences, and evolutionary biology. Research in this area aims to understand both how agents can…
Autonomous Vehicle (AV) technology is advancing rapidly, promising a significant shift in road transportation safety and potentially resolving various complex transportation issues. With the increasing deployment of AVs by various…
Any community in which membership is optional may eventually break apart, or fork. For example, forks may occur in political parties, business partnerships, social groups, cryptocurrencies, and federated governing bodies. Forking is…
Consider n agents forming an egalitarian, self-governed community. Their first task is to decide on a decision rule to make further decisions. We start from a rather general initial agreement on the decision-making process based upon a set…
The executive branch, or government, is typically not elected directly by the people, but rather formed by another elected body or person such as the parliament or the president. As a result, its members are not directly accountable to the…
While most of humanity is suddenly on the net, the value of this singularity is hampered by the lack of credible digital identities: Social networking, person-to-person transactions, democratic conduct, cooperation and philanthropy are all…
We present a unifying framework encompassing many social choice settings. Viewing each social choice setting as voting in a suitable metric space, we consider a general model of social choice over metric spaces, in which---similarly to the…
The coordination between agents in multi-agent systems has become a popular topic in many fields. To catch the inner relationship between agents, the graph structure is combined with existing methods and improves the results. But in…
In this work we analyze Multi-Agent Advantage Actor-Critic (MA2C) a recently proposed multi-agent reinforcement learning algorithm that can be applied to adaptive traffic signal control (ATSC) problems. To evaluate its potential we compare…
We study viral transmission in crowds via the short-ranged airborne pathway using a purely model-based approach. Our goal is two-pronged. Firstly, we illustrate with a concrete and pedagogical case study how to estimate the risks of new…
Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and has made progress in various fields. Specifically, cooperative MARL focuses on training a team of agents to cooperatively achieve tasks that are…
The goal of this research is to devise guaranteed defense policies that allow to protect a given region from the entrance of smart mobile invaders by detecting them using a team of defending agents equipped with identical line sensors. By…
Heterogeneous robots equipped with multi-modal sensors (e.g., UAV, wheeled and legged terrestrial robots) provide rich and complementary functions that may help human operators to accomplish complex tasks in unknown environments. However,…
Agent-based simulations, especially those including communication, are complex to model and execute. To help researchers deal with this complexity and to encourage modular and maintainable research software, the Python-based framework mango…
In economic modeling, there has been an increasing investigation into multi-agent simulators. Nevertheless, state-of-the-art studies establish the model based on reinforcement learning (RL) exclusively for specific agent categories, e.g.,…