Artificial Agents
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MPI allows the model to be independent of hardware platforms involved
in the implementation, allowing messages to be packed into certain
binders to be sent across nodes easily and independently. A message
board is set up from which all agents can read information and perform
tasks. To ensure that all functions are in order, there are synchronization
points inserted in the model where all data are regulated before moving
into the next block of functions.
The synchronization points ensure all memory is synchronized among
agents before the agents move into the next block of functions. It is
important to synchronize the distributed data so that all agents are
aware of information update.
SOAR. Developed in 1983, SOAR is continually being used as an architecture for developing intelligent systems. SOAR has been developed on
“the hypothesis that all deliberate goal-oriented behavior can be cast
as the selection and application of operators to a state” [111]. A state
is a representation of the problem-solving situation and goal as desired
outcome of the problem.
Based on [112] and [140], intelligence can be functionally described as
goals and realizable fundamentals. Newell described that all problems
can be broken down into smaller units and solutions of smaller units
can be unified for the larger perspective. SOAR allows features like longterm memory, using different memories for different situations and using
state hierarchy, where states represent a memory situation. SOAR [84] is
being used for military training purposes by the U.S. army, where some
work depicts how emotions affect soldier decisions during warfare with
emotional agents, making them less predictable to study real situations.
SimAgent. SimAgent toolkit produced at the University of Birmingham was
developed as part of the Cognition and Affect project. The project is
specifically targeted to design human-like agents and study effects of
learning, feeling and emotions. The authors [182] argued that the framework was developed to explore the ‘architectural design requirements for
intelligent human-like agents’.
35
MPI allows the model to be independent of hardware platforms involved
in the implementation, allowing messages to be packed into certain
binders to be sent across nodes easily and independently. A message
board is set up from which all agents can read information and perform
tasks. To ensure that all functions are in order, there are synchronization
points inserted in the model where all data are regulated before moving
into the next block of functions.
The synchronization points ensure all memory is synchronized among
agents before the agents move into the next block of functions. It is
important to synchronize the distributed data so that all agents are
aware of information update.
SOAR. Developed in 1983, SOAR is continually being used as an architecture for developing intelligent systems. SOAR has been developed on
“the hypothesis that all deliberate goal-oriented behavior can be cast
as the selection and application of operators to a state” [111]. A state
is a representation of the problem-solving situation and goal as desired
outcome of the problem.
Based on [112] and [140], intelligence can be functionally described as
goals and realizable fundamentals. Newell described that all problems
can be broken down into smaller units and solutions of smaller units
can be unified for the larger perspective. SOAR allows features like longterm memory, using different memories for different situations and using
state hierarchy, where states represent a memory situation. SOAR [84] is
being used for military training purposes by the U.S. army, where some
work depicts how emotions affect soldier decisions during warfare with
emotional agents, making them less predictable to study real situations.
SimAgent. SimAgent toolkit produced at the University of Birmingham was
developed as part of the Cognition and Affect project. The project is
specifically targeted to design human-like agents and study effects of
learning, feeling and emotions. The authors [182] argued that the framework was developed to explore the ‘architectural design requirements for
intelligent human-like agents’.