:modeling and control of large-size agent populations
First Statement of Responsibility
/ Dejan Lj. Milutinovic and Pedro U. Lima
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Berlin ; New York
Name of Publisher, Distributor, etc.
: Springer,
Date of Publication, Distribution, etc.
, c2007.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
xviii, 124 p. , ill. (some col.) , 24 cm.
SERIES
Series Title
(Springer tracts in advanced robotics 1610-7438
Volume Designation
; v. 32)
NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
Text of Note
Electronic
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references (p. [117]-121) and index.
CONTENTS NOTE
Text of Note
"Cells and Robots is an outcome of the multidisciplinary research extending over Biology, Robotics and Hybrid Systems Theory. It is inspired by modeling reactive behavior of the immune system cell population, where each cell is considered as an independent agent. In our modeling approach, there is no difference if the cells are naturally or artificially created agents, such as robots. This appears even more evident when we introduce a case study concerning a large-size robotic population scenario. Under this scenario, we also formulate the optimal control of maximizing the probability of robotic presence in a given region and discuss the application of the Minimum Principle for partial differential equations to this problem. Simultaneous consideration of cell and robotic populations is of mutual benefit for Biology and Robotics, as well as for the general understanding of multi-agent system dynamics."--BOOK JACKET.
Text of Note
1. Introduction -- 2. Immune systems and T-cell receptor dynamics of a T-cell population -- 3. Micro-agent and stochastic micro-agent models -- 4. Micro-agent population dynamics -- 5. Stochastic micro-agent model of the T-cell receptor dynamics -- 6. Stochastic micro-agent model uncertainties -- 7. Stochastic modeling and control of a large-size robotic population -- 8. Conclusions and future work -- A. Stochastic model and data processing of flow cytometry measurements -- B. Estimated T-cell receptor probability density function -- C. Steady state T-cell receptor probability density function and average amount -- D. Optimal control of partial differential equations.