Real-time Decision Making for Autonomous Systems using AI Methods

Abstract

In the last performance period, we performed an extensive review on the current status of MAS technologies, and developed new task allocation algorithms, focusing on the first four capabilities above providing certain optimality guarantee, reducing computation, and improving the adaptability and robustness. First two baseline algorithms were developed Decentralised decreasing Threshold Task Allocation (DTTA) and Decentralised Sample Threshold Task Allocation (STTA) and then improved to more practical versions Lazy Decreasing Threshold Task Allocation (LDTTA) and Threshold Bundle Task Allocation (TBTA). It was validated that the proposed algorithms achieve solution quality which is comparable to state-of-the-art algorithms in the monotone case, and much better quality in the non-monotone case with significantly less computational and communication complexity.

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Document Details

Document Type
Technical Report
Publication Date
Dec 12, 2023
Accession Number
AD1224985

Entities

People

  • Hyo-Sang Shin

Organizations

  • Cranfield University

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Operations Research

Technology Areas

  • Autonomy
  • Autonomy - Autonomous System Control