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Intelligent Knowledge Distribution for Multi-Agent Communication, Planning, and Learning

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dc.contributor.authorFowler, Michael C.en
dc.contributor.committeechairWilliams, Ryan K.en
dc.contributor.committeechairClancy, Thomas Charles IIIen
dc.contributor.committeememberTokekar, Pratapen
dc.contributor.committeememberPatterson, Cameron D.en
dc.contributor.committeememberRoan, Michael J.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2020-05-07T08:01:15Zen
dc.date.available2020-05-07T08:01:15Zen
dc.date.issued2020-05-06en
dc.description.abstractThis dissertation addresses a fundamental question of multi-agent coordination: what infor- mation should be sent to whom and when, with the limited resources available to each agent? Communication requirements for multi-agent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multi-agent coordination on networked systems, e.g., power and bandwidth, this dissertation introduces new concepts to enable Intelligent Knowledge Distribution (IKD), including Constrained-action POMDPs (CA-POMDP) and concurrent decentralized (CoDec) POMDPs for an agnostic plug-and-play capability for fully autonomous systems. Each agent runs a CoDec POMDP where all the decision making (motion planning, task allocation, asset monitoring, and communication) are separated into concurrent individual MDPs to reduce the combinatorial explosion of the action and state space while maintaining dependencies between the models. We also introduce the CA-POMDP with action-based constraints on partially observable Markov decision processes, rewards driven by the value of information, and probabilistic constraint satisfaction through discrete optimization and Markov chain Monte Carlo analysis. IKD is adapted real-time through machine learning of the actual environmental impacts on the behavior of the system, including collaboration strategies between autonomous agents, the true value of information between heterogeneous systems, observation probabilities and resource utilization.en
dc.description.abstractgeneralThis dissertation addresses a fundamental question behind when multiple autonomous sys- tems, like drone swarms, in the field need to coordinate and share data: what information should be sent to whom and when, with the limited resources available to each agent? Intelligent Knowledge Distribution is a framework that answers these questions. Communication requirements for multi-agent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multi-agent coordination on networked systems, e.g., power and bandwidth, this dissertation introduces new concepts to enable Intelligent Knowledge Distribution (IKD), including Constrained-action POMDPs and concurrent decentralized (CoDec) POMDPs for an agnostic plug-and-play capability for fully autonomous systems. The IKD model was able to demonstrate its validity as a "plug-and-play" library that manages communications between agents that ensures the right information is being transmitted at the right time to the right agent to ensure mission success.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:25109en
dc.identifier.urihttp://hdl.handle.net/10919/97996en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectMulti-agent Systemen
dc.subjectDistributed Decision Makingen
dc.subjectMarkov Decision Processesen
dc.subjectRelational Learningen
dc.subjectProbabilistic Constraint Satisfactionen
dc.subjectWireless Communicationsen
dc.titleIntelligent Knowledge Distribution for Multi-Agent Communication, Planning, and Learningen
dc.typeDissertationen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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