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X-Machines for Agent-Based Modeling: FLAME Perspectives
“We need a facility for rapidly implementing and testing out different
agent architectures, including scenarios where each agent is composed of
several different sorts of concurrent interacting sub-systems, in an environment where there are other agents and objects. Some agents should
have sensors and effectors, and some should be allowed to communicate
with others. Some agents should have hybrid architectures including, for
example, symbolic mechanisms communicating with neural nets. We also
wanted to be able to use the toolkit for exploring evolutionary processes,
as in the ‘Blind and Lazy’ scenario.”
SimAgent has been coded using Pop-11 and Poplog. The programming
paradigms [182] use object-oriented programming based on ObjectClass
extension to Pop-11. Rule-based programming and pattern matching
are also based on Pop-11 with a Pop rule base library. The framework is
event-driven, where the toolkit allows events for instance, if the mouse
is used to move an obstacle across the scenario, the agent would dynamically calculate their positions and change their walk direction.
Poplog supports Prolog to allow rules and behavior code for logic programming. This allows neural networks to be coded separately and
tested [182]. SimAgent also uses an RCLib package for various tests,
using neural networks to implement how feelings are handled. Figure
2.4 gives a depiction of how an agent with thinking capabilities is visualized.
Netlogo. Netlogo is a multi-agent programmable modeling environment and
is one of the most famous platforms. It allows modelers to give instructions to hundreds or thousands of ‘agents’ operating independently. This
feature makes it possible to explore the connection between the microlevel behavior of individuals and the macro-level patterns that emerge
from interactions of many individuals. One of the platform’s most efficient feature is its graphical user interface, which provides users with a
wide variety of options that can be used to manage models. The GUI is
user friendly and very easy to navigate.
Repast. Recursive Porus Agent Simulation Toolkit [7] developed in Java and
exploits all of its functionalities. Supported as an open source project,
new versions of Repast Symphony can handle high performance computing (HPC) grids and have easy-to-use interfaces for building and
modeling agents. The platform can be used to create, run, display and
collect data from agent-based simulations and is fully object oriented.
Repast is a toolkit with a wide variety of tools and structures. Similar to
Netlogo, it also provides an efficient graphical user interface that users
can use to manage models, manipulate parameters, set output data and
show agent interactions in detail.
X-Machines for Agent-Based Modeling: FLAME Perspectives
“We need a facility for rapidly implementing and testing out different
agent architectures, including scenarios where each agent is composed of
several different sorts of concurrent interacting sub-systems, in an environment where there are other agents and objects. Some agents should
have sensors and effectors, and some should be allowed to communicate
with others. Some agents should have hybrid architectures including, for
example, symbolic mechanisms communicating with neural nets. We also
wanted to be able to use the toolkit for exploring evolutionary processes,
as in the ‘Blind and Lazy’ scenario.”
SimAgent has been coded using Pop-11 and Poplog. The programming
paradigms [182] use object-oriented programming based on ObjectClass
extension to Pop-11. Rule-based programming and pattern matching
are also based on Pop-11 with a Pop rule base library. The framework is
event-driven, where the toolkit allows events for instance, if the mouse
is used to move an obstacle across the scenario, the agent would dynamically calculate their positions and change their walk direction.
Poplog supports Prolog to allow rules and behavior code for logic programming. This allows neural networks to be coded separately and
tested [182]. SimAgent also uses an RCLib package for various tests,
using neural networks to implement how feelings are handled. Figure
2.4 gives a depiction of how an agent with thinking capabilities is visualized.
Netlogo. Netlogo is a multi-agent programmable modeling environment and
is one of the most famous platforms. It allows modelers to give instructions to hundreds or thousands of ‘agents’ operating independently. This
feature makes it possible to explore the connection between the microlevel behavior of individuals and the macro-level patterns that emerge
from interactions of many individuals. One of the platform’s most efficient feature is its graphical user interface, which provides users with a
wide variety of options that can be used to manage models. The GUI is
user friendly and very easy to navigate.
Repast. Recursive Porus Agent Simulation Toolkit [7] developed in Java and
exploits all of its functionalities. Supported as an open source project,
new versions of Repast Symphony can handle high performance computing (HPC) grids and have easy-to-use interfaces for building and
modeling agents. The platform can be used to create, run, display and
collect data from agent-based simulations and is fully object oriented.
Repast is a toolkit with a wide variety of tools and structures. Similar to
Netlogo, it also provides an efficient graphical user interface that users
can use to manage models, manipulate parameters, set output data and
show agent interactions in detail.