Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Evolving aggregation behaviors for swarm robotics systems: a systematic case study
Download
index.pdf
Date
2005
Author
Bahçeci, Erkin
Metadata
Show full item record
Item Usage Stats
86
views
69
downloads
Cite This
Evolutionary methods are shown to be useful in developing behaviors in robotics. Interest in the use of evolution in swarm robotics is also on the rise. However, when one attempts to use artificial evolution to develop behaviors for a swarm robotic system, he is faced with decisions to be made regarding some parameters of fitness evaluations and of the genetic algorithm. In this thesis, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron controllers that are evolved for a simulated swarm robotic system are systematically studied with different parameter settings. Using a cluster of computers to run simulations in parallel, four experiments are conducted varying some of the parameters. Rules of thumb are derived, which can be of guidance to the use of evolutionary methods to generate other swarm robotic behaviors as well.
Subject Keywords
Electronic computers.
URI
http://etd.lib.metu.edu.tr/upload/12606315/index.pdf
https://hdl.handle.net/11511/15243
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
A mathematical contribution of statistical learning and continuous optimization using infinite and semi-infinite programming to computational statistics
Özöğür-Akyüz, Süreyya; Weber, Gerhard Wilhelm; Department of Scientific Computing (2009)
A subfield of artificial intelligence, machine learning (ML), is concerned with the development of algorithms that allow computers to “learn”. ML is the process of training a system with large number of examples, extracting rules and finding patterns in order to make predictions on new data points (examples). The most common machine learning schemes are supervised, semi-supervised, unsupervised and reinforcement learning. These schemes apply to natural language processing, search engines, medical diagnosis,...
Robot planing based on learned affordances
Çakmak, Maya; Şahin, Erol; Department of Computer Engineering (2007)
This thesis studies how an autonomous robot can learn affordances from its interactions with the environment and use these affordances in planning. It is based on a new formalization of the concept which proposes that affordances are relations that pertain to the interactions of an agent with its environment. The robot interacts with environments containing different objects by executing its atomic actions and learns the different effects it can create, as well as the invariants of the environments that aff...
A hypercomputational approach to the agent causation theory of free will
Mersin, Serhan; Sayan, Erdinç; Department of Cognitive Sciences (2006)
Hypercomputation, which is the general concept embracing all machinery capable of carrying out more tasks than Turing Machines and beyond the Turing Limit, has implications for various fields including mathematics, physics, computer science and philosophy. Regarding its philosophical aspects, it is necessary to reveal the position of hypercomputation relative to the classical computational theory of mind in order to clarify and broaden the scope of hypercomputation so that it encompasses some phenomena whic...
Using learned affordances for robotic behavior development
Doğar, Mehmet Remzi; Şahin, Erol; Department of Civil Engineering (2007)
“Developmental robotics” proposes that, instead of trying to build a robot that shows intelligence once and for all, what one must do is to build robots that can develop. A robot should go through cognitive development just like an animal baby does. These robots should be equipped with behaviors that are simple but enough to bootstrap the system. Then, as the robot interacts with its environment, it should display increasingly complex behaviors. Studies in developmental psychology and neurophysiology provid...
Comparison of rough multi layer perceptron and rough radial basis function networks using fuzzy attributes
Vural, Hülya; Alpaslan, Ferda Nur; Department of Computer Engineering (2004)
The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties أlow...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. Bahçeci, “Evolving aggregation behaviors for swarm robotics systems: a systematic case study,” M.S. - Master of Science, Middle East Technical University, 2005.