3,102 research outputs found
Learning-based Physical Layer Communications for Multiagent Collaboration
Consider a collaborative task carried out by two autonomous agents that can communicate over a noisy channel. Each agent is only aware of its own state, while the accomplishment of the task depends on the value of the joint state of both agents. As an example, both agents must simultaneously reach a certain location of the environment, while only being aware of their own positions. Assuming the presence of feedback in the form of a common reward to the agents, a conventional approach would apply separately: (\emph{i}) an off-the-shelf coding and decoding scheme in order to enhance the reliability of the communication of the state of one agent to the other; and (\emph{ii}) a standard multiagent reinforcement learning strategy to learn how to act in the resulting environment. In this work, it is argued that the performance of the collaborative task can be improved if the agents learn how to jointly communicate and act. In particular, numerical results for a baseline grid world example demonstrate that the jointly learned policy carries out compression and unequal error protection by leveraging information about the action policy
Task-Based Information Compression for Multi-Agent Communication Problems with Channel Rate Constraints
A collaborative task is assigned to a multiagent system (MAS) in which agents
are allowed to communicate. The MAS runs over an underlying Markov decision
process and its task is to maximize the averaged sum of discounted one-stage
rewards. Although knowing the global state of the environment is necessary for
the optimal action selection of the MAS, agents are limited to individual
observations. The inter-agent communication can tackle the issue of local
observability, however, the limited rate of the inter-agent communication
prevents the agent from acquiring the precise global state information. To
overcome this challenge, agents need to communicate their observations in a
compact way such that the MAS compromises the minimum possible sum of rewards.
We show that this problem is equivalent to a form of rate-distortion problem
which we call the task-based information compression. We introduce a scheme for
task-based information compression titled State aggregation for information
compression (SAIC), for which a state aggregation algorithm is analytically
designed. The SAIC is shown to be capable of achieving near-optimal performance
in terms of the achieved sum of discounted rewards. The proposed algorithm is
applied to a rendezvous problem and its performance is compared with several
benchmarks. Numerical experiments confirm the superiority of the proposed
algorithm.Comment: 13 pages, 9 figure
Multi Agent Systems in Logistics: A Literature and State-of-the-art Review
Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems
Is Ambient Intelligence a truly Human-Centric Paradigm in Industry? Current Research and Application Scenario
The use of pervasive networked devices is nowadays a reality in the service sector. It impacts almost all aspects of our daily lives, although most times we are not aware of its influence. This is a fundamental characteristic of the concept of Ambient Intelligence (AmI). Ambient Intelligence aims to change the form of human-computer interaction, focusing on the user needs so they can interact in a more seamless way, with emphasis on greater user-friendliness. The idea of recognizing people and their context situation is not new and has been successfully applied with limitations, for instance, in the health and military sectors. However its appearance in the manufacturing industry has been elusive. Could the concept of AmI turn the current shop floor into a truly human centric environment enabling comprehensive reaction to human presence and action? In this article an AmI scenario is presented and detailed with applications in human’s integrity and safety.Ambient Intelligence, networks, human-computer interaction
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
Learning agents that are not only capable of taking tests, but also
innovating is becoming a hot topic in AI. One of the most promising paths
towards this vision is multi-agent learning, where agents act as the
environment for each other, and improving each agent means proposing new
problems for others. However, existing evaluation platforms are either not
compatible with multi-agent settings, or limited to a specific game. That is,
there is not yet a general evaluation platform for research on multi-agent
intelligence. To this end, we introduce Arena, a general evaluation platform
for multi-agent intelligence with 35 games of diverse logics and
representations. Furthermore, multi-agent intelligence is still at the stage
where many problems remain unexplored. Therefore, we provide a building toolkit
for researchers to easily invent and build novel multi-agent problems from the
provided game set based on a GUI-configurable social tree and five basic
multi-agent reward schemes. Finally, we provide Python implementations of five
state-of-the-art deep multi-agent reinforcement learning baselines. Along with
the baseline implementations, we release a set of 100 best agents/teams that we
can train with different training schemes for each game, as the base for
evaluating agents with population performance. As such, the research community
can perform comparisons under a stable and uniform standard. All the
implementations and accompanied tutorials have been open-sourced for the
community at https://sites.google.com/view/arena-unity/
Large network multi-level control for CAV and Smart Infrastructure: AI-based Fog-Cloud collaboration
Application of Decentralized and Self-Regulating Knowledge Bases for Assembly Design Automation
During product development, changes to parts that are already built into assemblies usually lead to the need to check the function and consistency of the assembly. This procedure is very time-consuming and has to be performed again for each change. In this paper, an approach is presented in which the individual parts are represented as agents that adapt themselves to new conditions. The agents are combined in a multi-agent system (MAS) and interact via communication over messages. For this purpose, a methodical procedure for the development of the MAS and the implementation in a CAD development environment is presented. The validation of the MAS is carried out on the application example of a gearbox
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