Artificial Agents
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JADE. Java Agent DEvelopment framework (JADE) is an agent platform,
developed completely in Java and uses Remote Method Invocation
(RMI) registry for concurrent connection between machines. Every agent
can be defined as a thread, which can simulate as a hierarchy of behaviors. All agents inherit from a class of super agents for common
attributes.
JADE is based on standards such as FIPA (Foundation for Intelligent
Physical Agents) protocols, used as standard communications languages
for agent and environment communication.
MASON. MASON is a multi-agent simulation toolkit that allows discrete
events to be simulated. Written in Java, it includes a 2D and 3D library
for visualization. MASON has been used to develop ECJ, as a Java-based
Evolutionary Computation Research System [123]. ECJ is claimed to be
highly flexible with classes dynamically compiled at runtime by a userprovided parameter file.
TAEMS. TAEMS (Task Analysis, Environmental Modeling and Simulation)
is described as a ‘formal, domain-independent framework’ which attempts to solve problems for intelligent agents in different scenarios.
The language produces a hierarchical structure of tasks the agent has
to perform and assesses them according to goals and deadlines. This
hierarchical structure can be viewed as a distributed goal tree, in which
branches are joined by AND or OR operations, to produce combinations
in scenarios with limited resources and decision-making.
2.4 Adaptive Agent Design
Agents can be designed as either a logical machine with a set of actions
or with artificial intelligence, as a set of controllers associated with actions,
or with psychology, to mimic minds of real people. However, mimicking the
mind of real people is a laborious task and also presents a potential problem
to computational complexities of code. Some reasons for this could be the
vast amount of memory required, or processing time to pool out relevant
information and process it to determine the next action of the agent.
Most researchers have adopted their own methods to achieve the mind
in their models. Dawid [47] and Vriend [200] have explored use of genetic
algorithms for making economic decisions. Sometimes these algorithms can
be calibrated to depict decision-making situations, like in works of LeBaron
[115] and Marks [127]. Duffy [55] used human subjects as experimental data
to calibrate learning in computational agents.
Researchers have often debated that learning architectures in agents can
37
JADE. Java Agent DEvelopment framework (JADE) is an agent platform,
developed completely in Java and uses Remote Method Invocation
(RMI) registry for concurrent connection between machines. Every agent
can be defined as a thread, which can simulate as a hierarchy of behaviors. All agents inherit from a class of super agents for common
attributes.
JADE is based on standards such as FIPA (Foundation for Intelligent
Physical Agents) protocols, used as standard communications languages
for agent and environment communication.
MASON. MASON is a multi-agent simulation toolkit that allows discrete
events to be simulated. Written in Java, it includes a 2D and 3D library
for visualization. MASON has been used to develop ECJ, as a Java-based
Evolutionary Computation Research System [123]. ECJ is claimed to be
highly flexible with classes dynamically compiled at runtime by a userprovided parameter file.
TAEMS. TAEMS (Task Analysis, Environmental Modeling and Simulation)
is described as a ‘formal, domain-independent framework’ which attempts to solve problems for intelligent agents in different scenarios.
The language produces a hierarchical structure of tasks the agent has
to perform and assesses them according to goals and deadlines. This
hierarchical structure can be viewed as a distributed goal tree, in which
branches are joined by AND or OR operations, to produce combinations
in scenarios with limited resources and decision-making.
2.4 Adaptive Agent Design
Agents can be designed as either a logical machine with a set of actions
or with artificial intelligence, as a set of controllers associated with actions,
or with psychology, to mimic minds of real people. However, mimicking the
mind of real people is a laborious task and also presents a potential problem
to computational complexities of code. Some reasons for this could be the
vast amount of memory required, or processing time to pool out relevant
information and process it to determine the next action of the agent.
Most researchers have adopted their own methods to achieve the mind
in their models. Dawid [47] and Vriend [200] have explored use of genetic
algorithms for making economic decisions. Sometimes these algorithms can
be calibrated to depict decision-making situations, like in works of LeBaron
[115] and Marks [127]. Duffy [55] used human subjects as experimental data
to calibrate learning in computational agents.
Researchers have often debated that learning architectures in agents can