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For each new customer, we instantiate our generic meta-model into a specific problem description expressed in YAML or JSON. Then the problem description is automatically translated into an actual multi-agent system.

As soon as possible, graphical models produced with this tool are refined to instantiate an actual multi-agent system. This is done through high-level descriptions that are basically textual translations of the graphical models, except that they allow for more details. This problem description is to be used as an input

From a configuration point of view, the Run Logic describes the model. Then a specific API instantiates the Pricefx generic meta-model into a problem description expressed in YAML. Then the YAML problem description is automatically translated into an actual multi-agent system and can be run.

In this section, all the code snippets are in YAML, since it is the last format, understandable by the Optimization Engine backend.

The steps to create a specific model description are:

1. Run.groovy Logic: understand its structure

2. Define the hierarchies

3. Define the spaces

4. Define the scopes inside each space

5. Define the variables inside each scope

6. Define the criteria inside each scope

Understanding the underlying concepts is important. Page Main Concepts for Optimization Problems is the reference.

This section also provides a problem description sample.

Further in this section: