Minimizing Movement: Fixed-Parameter Tractability

Abstract

We study an extensive class of movement minimization problems that arise from many practical scenarios but so far have little theoretical study. In general, these problems involve planning the coordinated motion of a collection of agents (representing robots, people, map labels, network messages, etc.) to achieve a global property in the network while minimizing the maximum or average movement (expended energy). The only previous theoretical results about this class of problems are about approximation and are mainly negative: many movement problems of interest have polynomial inapproximability. Given that the number of mobile agents is typically much smaller than the complexity of the environment, we turn to fixed-parameter tractability. We characterize the boundary between tractable and intractable movement problems in a very general setup: it turns out the complexity of the problem fundamentally depends on the treewidth of the minimal configurations. Thus, the complexity of a particular problem can be determined by answering a purely combinatorial question. Using our general tools, we determine the complexity of several concrete problems and fortunately show that many movement problems of interest can be solved efficiently.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 30, 2014
Source ID
10.1145/2650247

Entities

People

  • Daniel Marx
  • Erik D. Demaine
  • MohammadTaghi Hajiaghayi

Organizations

  • Air Force Office of Scientific Research
  • Defense Advanced Research Projects Agency
  • Division of Computing and Communication Foundations
  • European Research Council
  • Hungarian Scientific Research Fund
  • Institute for Computer Science and Control of the Hungarian Academy of Sciences
  • Massachusetts Institute of Technology
  • National Science Foundation
  • Office of Naval Research
  • University of Maryland

Tags

Fields of Study

  • Computer science

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy