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dc.contributor.authorMeshref, Hossamen_US
dc.date.accessioned2014-03-14T20:14:36Z
dc.date.available2014-03-14T20:14:36Z
dc.date.issued2002-07-09en_US
dc.identifier.otheretd-08012002-183248en_US
dc.identifier.urihttp://hdl.handle.net/10919/28473
dc.description.abstractAutonomous robots are expected to interact with their dynamic changing environment. This interactions requires certain level of behavior based Intelligence, which facilitates the dynamic adaptation of the robot behavior accordingly with his surrounding environment. Many researches have been done in biological information processing systems to model the behavior of an autonomous robot. The Artificial Immune System (AIS) provides new paradigm suitable for dynamic problem dealing with unknown environment rather than a static problem. The immune system has some features such as memory, tolerance, diversity and more features that can be used in engineering applications. The immune system has an important feature called meta-dynamics in which new species of antibodies are produced continuously from the bone marrow. If the B-Cell (robot) cannot deal with the current situation, new behaviors (antibodies) should be generated by the meta dynamics function. This behavior should be incorporated into the existing immune system to gain immunity against new environmental changes. We decided to use a feed forward Artificial Neural Network (ANN) to simulate this problem, and to build the AIS memory. Many researchers have tried to tackle different points in mimicking the biological immune system, but no one previously has proposed such an acquired memory. This contribution is made as a "proof of concept" to the field of biological immune system simulation as a start of further research efforts in this direction. Many applications can potentially use our designed Neuro-Immune Network (NIN), especially in the area of autonomous robotics. We demonstrated the use of the designed NIN to control a robot arm in an unknown environment. As the system encounters new cases, it will increase its ability to deal with old and new situations encountered. This novel technique can be applied to many robotics applications in industry, where autonomous robots are required to have adaptive behavior in response to their environmental changes. Regarding future work, the use of VLSI neural networks to enhance the speed of the system for real time applications can be investigated along with possible methods of design and implementation of a similar VLSI chip for the AIN.en_US
dc.publisherVirginia Techen_US
dc.relation.haspart03_ListOfFigures.pdfen_US
dc.relation.haspart12_References&Vita.pdfen_US
dc.relation.haspart11_Conclusion.pdfen_US
dc.relation.haspart02_TableOfContents.pdfen_US
dc.relation.haspart05_Chapter1.pdfen_US
dc.relation.haspart01_Acknowledgment.pdfen_US
dc.relation.haspart00_Cover&Abstract.pdfen_US
dc.relation.haspart10_Chapter6.pdfen_US
dc.relation.haspart09_Chapter5.pdfen_US
dc.relation.haspart04_ListOfTables.pdfen_US
dc.relation.haspart08_Chapter4.pdfen_US
dc.relation.haspart07_Chapter3.pdfen_US
dc.relation.haspart06_Chapter2.pdfen_US
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectImmune Systemsen_US
dc.subjectAutonomous Roboticsen_US
dc.subjectNeural Networksen_US
dc.titleModeling Autonomous Agents' Behavior Using Neuro-Immune Networksen_US
dc.typeDissertationen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.committeechairVanLandingham, Hugh F.en_US
dc.contributor.committeememberReinholtz, Charles F.en_US
dc.contributor.committeememberBaumann, William T.en_US
dc.contributor.committeememberElshabini-Riad, Aicha A.en_US
dc.contributor.committeememberKachroo, Pushkinen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08012002-183248/en_US
dc.date.sdate2002-08-01en_US
dc.date.rdate2003-08-22
dc.date.adate2002-08-22en_US


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