Is anyone using the ³Factory² design pattern to construct agents at run
time? I am about halfway down this path and Iıd love to learn from anyone
else who has experience with this approach. (See ³Difficulties² below)
Requirements
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The setting is modeling teams and organizations engaged in design (of
artificial products), innovation (in how they do design), and qualitative
learning (i.e. learning new skills, not just getting better at existing
skills). In other words, Iım modeling endogenous innovation involving
genuine novelty. My agents are (somewhat) cognitive and adaptive,
particularly in their ability to acquire or develop novel capabilities
during the course of a simulation run. Also Iıd like to experiment with a
wide variety of initial conditions regarding agent types and capabilities.
In essence, what Iım looking for is agents that are dynamic bundles of
capabilities (i.e. resources, action rules, etc.) rather than fixed
capabilities as is the norm in most ABMs.
Approach
-------------
These requirements suggest the use of a ³Factory² design pattern (see
https://en.wikipedia.org/wiki/Factory_method_pattern for general
description).
During start( ), the AgentFactory is an object that takes as input
specifications for the agents (i.e. list of capabilities) and also other key
objects (i.e. environment objects such as SimState). The AgentFactory class
includes various pre-built templates and objects to produce an agent object
of the specified type. (I have a large library of capability and processing
objects.) Actions to be performed by agent at each time step is specified
by a special-purpose language that describe sequences of tasks and their
interdependencies. (Execution is by a simple O-O stack machine.) Each task
execution records its results, and these results can be concatenated and
logged in a file. Essentially, agents can ³explain² what they did and why
each step. This is useful for both debugging and also as experimental
results.
Clearly, this approach does not result in fast execution and would not be
suitable for a large population of agents and long simulation runs. My
simulations will involve tens or at most a hundred agents, so it wonıt be a
problem for me.
Difficulties
--------------
Where Iım running into difficulty is in software design decisions: 1) how
general-purpose to make the code, and 2) how to avoid unnecessary
complications. The advantage of making it fully general-purpose is that it
will (later) reduce the design and debug time for specific ABM designs. The
disadvantage is that it takes more time now for thinking, planning, coding,
and testing (e.g. debug capabilities for the special-purpose stack machine).
In pursuit of general functionality, I am probably making the software
design too complicated. It would be nice to see what other people have done
and maybe learn from their experience.
Also, I want to use built-in capabilities of Java and also available Java
libraries. For example, when I get to the point where agent specifications
will come from a text file, Iıll probably use ANTLR to parse my
special-purpose task language. But I might be unknowingly ³recreating the
wheel² in many cases by creating my own classes for various low-level
functions.
Thanks,
Russell Thomas
Computational Social Science
George Mason University
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