Towards Hypermedia Environments for Adaptive Coordination in Industrial Automation

• URL: http://arxiv.org/abs/2406.17816v1
• Date: Tue, 25 Jun 2024 06:21:52 GMT
• Title: Towards Hypermedia Environments for Adaptive Coordination in Industrial Automation
• Authors: Ganesh Ramanathan, Simon Mayer, Andrei Ciortea,
• Abstract summary: Electromechanical systems manage physical processes through a network of inter-connected components. We use autonomous software agents that process semantic descriptions of the system to determine coordination requirements and constraints. Agents then interact with one another to control the system in a decentralized and coordinated manner.
• Score: 3.686808512438363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electromechanical systems manage physical processes through a network of inter-connected components. Today, programming the interactions required for coordinating these components is largely a manual process. This process is time-consuming and requires manual adaptation when system features change. To overcome this issue, we use autonomous software agents that process semantic descriptions of the system to determine coordination requirements and constraints; on this basis, they then interact with one another to control the system in a decentralized and coordinated manner.Our core insight is that coordination requirements between individual components are, ultimately, largely due to underlying physical interdependencies between the components, which can be (and, in many cases, already are) semantically modeled in automation projects. Agents then use hypermedia to discover, at run time, the plans and protocols required for enacting the coordination. A key novelty of our approach is the use of hypermedia-driven interaction: it reduces coupling in the system and enables its run-time adaptation as features change.

Related papers

• Communication Learning in Multi-Agent Systems from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We introduce a temporal gating mechanism for each agent, enabling dynamic decisions on whether to receive shared information at a given time.
  arXiv  Detail & Related papers  (2024-11-01T05:56:51Z)
• Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots [1.1049608786515839]
  This paper presents the Cooperative and Asynchronous Transformer-based Mission Planning (CATMiP) framework. CatMiP uses multi-agent reinforcement learning to coordinate agents with heterogeneous sensing, motion, and actuation capabilities. It easily adapts to mission complexities and communication constraints, and scales to varying environment sizes and team compositions.
  arXiv  Detail & Related papers  (2024-10-08T21:14:09Z)
• Towards the decentralized coordination of multiple self-adaptive systems [0.3222802562733786]
  When multiple self-adaptive systems share the same environment and have common goals, they may coordinate their adaptations at runtime to avoid conflicts and to satisfy their goals. We propose CoADAPT, a decentralized coordination technique introducing two types of constraints: preference constraints, expressing local concerns, and consistency constraints, expressing shared concerns. At runtime, the problem is solved in a decentralized way using distributed constraint optimization algorithms implemented by each self-adaptive system.
  arXiv  Detail & Related papers  (2023-10-26T08:22:35Z)
• Asynchronous Perception-Action-Communication with Graph Neural Networks [93.58250297774728]
  Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments. The robots must execute a Perception-Action-Communication loop -- they perceive their local environment, communicate with other robots, and take actions in real time. Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control. This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication.
  arXiv  Detail & Related papers  (2023-09-18T21:20:50Z)
• Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems [0.0]
  We propose the integration of large language models (LLMs) into multiagent systems. We anchor our methodology on the MAPE-K model, which is renowned for its robust support in monitoring, analyzing, planning, and executing system adaptations.
  arXiv  Detail & Related papers  (2023-07-12T14:26:46Z)
• Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments [54.39292848359306]
  We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic.
  arXiv  Detail & Related papers  (2023-03-27T01:50:45Z)
• Multi-Agent Reinforcement Learning for Pragmatic Communication and Control [40.11766545693947]
  We propose a joint design that combines goal-oriented communication and networked control into a single optimization model. Joint training of the communication and control systems can significantly improve the overall performance.
  arXiv  Detail & Related papers  (2023-02-28T08:30:24Z)
• A Unified Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams [0.0]
  We present a novel architecture for dynamic role allocation and collaborative task planning in a mixed human-robot team of arbitrary size. The architecture capitalizes on a centralized reactive and modular task-agnostic planning method based on Behavior Trees (BTs) Different metrics used as MILP cost allow the architecture to favor various aspects of the collaboration.
  arXiv  Detail & Related papers  (2023-01-19T12:30:56Z)
• Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes [78.120734120667]
  This paper describes an agent-based decentralized and integrated scheduling approach. Part of the requirements is to develop a linearly scaling communication architecture. The approach is explained using an example based on industrial requirements.
  arXiv  Detail & Related papers  (2022-05-06T18:44:29Z)
• Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
  Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
  arXiv  Detail & Related papers  (2020-07-13T20:30:26Z)
• Learning to Control PDEs with Differentiable Physics [102.36050646250871]
  We present a novel hierarchical predictor-corrector scheme which enables neural networks to learn to understand and control complex nonlinear physical systems over long time frames. We demonstrate that our method successfully develops an understanding of complex physical systems and learns to control them for tasks involving PDEs.
  arXiv  Detail & Related papers  (2020-01-21T11:58:41Z)

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.