Authors:
M. Li
1
;
C. Qiu
2
;
J. Park
1
;
D. Chan
1
;
J. Jeon
1
;
J. Na
1
;
C. Wong
1
;
B. Zhao
1
;
E. Chang
1
;
S. Kazadi
3
and
S. Hettiarachchi
4
Affiliations:
1
Jisan Research Institute, United States
;
2
University of California Santa Barbara, United States
;
3
Jisan Research Institute and Illinois Mathematics and Science Academy, United States
;
4
Indiana University Southeast, United States
Keyword(s):
Swarm Engineering, Hamiltonian Method of Swarm Design.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Autonomous Systems
;
Bioinformatics
;
Biomedical Engineering
;
Collective Intelligence
;
Cooperation and Coordination
;
Distributed and Mobile Software Systems
;
Distributed Problem Solving
;
Enterprise Information Systems
;
Group Decision Making
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Multi-Agent Systems
;
Operational Research
;
Self Organizing Systems
;
Simulation
;
Software Engineering
;
Symbolic Systems
Abstract:
We utilize a swarm design methodology that enables us to develop classes of swarm solutions to specific
specifications. The method utilizes metrics devised to evaluate the swarm’s progress – the global variables –
along with the set of available technologies in order to answer varied questions surrounding a swarm design
for the task. These questions include the question of whether or not a swarm is necessary for a given task. The
Jacobian matrix, here identified as the technology matrix, is created from the global variables. This matrix
may be interpreted in a way that allows the identification of classes of technologies required to complete the
task. This approach allows us to create a class of solutions that are all suitable for accomplishing the task. We
demonstrate this capability for accumulation swarms, generating several configurations that can be applied to
complete the task. If the technology required to complete the task either cannot be implemented on a single
agent or is
unavailable, it may be possible to utilize a swarm to generate the capability in a distributed way.
We demonstrate this using a gradient-based search task in which a minimal swarm is designed along with two
additional swarms, all of which extend the agents’ capabilities and successfully accomplish the task.
(More)