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Title page for ETD etd-03242015-132552


Type of Document Dissertation
Author Sen, Sayan Dev
Author's Email Address sayan.d.sen@vanderbilt.edu
URN etd-03242015-132552
Title An intelligent and unified framework for multiple robot and human coalition formation
Degree PhD
Department Computer Science
Advisory Committee
Advisor Name Title
Dr. Julie A. Adams Committee Chair
Dr. Douglas H. Fisher Committee Member
Dr. Gautam Biswas Committee Member
Dr. Nilanjan Sarkar Committee Member
Dr. Peter H. Stone Committee Member
Keywords
  • Multi-robot systems
  • Coalition formation
  • Swarm Intelligence
  • Optimization
Date of Defense 2015-01-21
Availability unrestricted
Abstract
Robotic systems have proven effective with recent deployments of unmanned robots in numerous missions. Teaming multiple agents requires efficient coalition formation, which is an NP-complete problem that is also hard to approximate within a reasonable factor. The computational complexity of the problem has led to the development of a number of greedy, approximation, and market-based solving techniques; however, no single algorithm can cater to a wide spectrum of mission situations. The primary contribution of this dissertation is the development of a unified framework, called i-CiFHaR, the first of its kind to incorporate a library of diverse coalition formation algorithms, each employing a different problem solving mechanism. i-CiFHaR employs unsupervised learning to mine crucial patterns among the algorithms and makes intelligent and optimized decisions over the library to select the most appropriate algorithm(s) to apply in accordance with multiple mission criteria by leveraging Bayesian reasoning.

The second major contribution of this dissertation adds to the state-of-the-art in swarm intelligence by presenting two novel hybrid ant colony optimization algorithms that are applicable to a wide spectrum of combinatorial optimization problems. The algorithms effectively address search stagnation, a common drawback of existing ant algorithms by leveraging novel pheromone update policies that integrate the simulated annealing methodology. The presented algorithms outperformed existing state-of-the-art ant algorithms when applied to three NP-complete problems in terms of solution quality by exhibiting a higher searching capability.

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