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Release v0.4.2
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## Supported export Models | ||
This is the list of currently supported models which MDF supports | ||
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- ACT-R [README](https://github.com/ModECI/MDF/tree/main/examples/ACT-R#readme) | ||
- NeuroML [README](https://github.com/ModECI/MDF/tree/main/examples/NeuroML#readme) | ||
- ONNX [README](https://github.com/ModECI/MDF/tree/main/examples/ONNX#readme) | ||
- PsyNeuLink [README](https://github.com/ModECI/MDF/tree/main/examples/PsyNeuLink#readme) | ||
- PyTorch [README](https://github.com/ModECI/MDF/tree/main/examples/PyTorch#readme) | ||
- Quantum [README](https://github.com/ModECI/MDF/tree/main/examples/Quantum#readme) | ||
- WebGME [README](https://github.com/ModECI/MDF/tree/main/examples/WebGME#readme) |
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# Interactions between MDF and ACT-R | ||
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This directory contains examples of ACT-R models converted to MDF. The ACT-R | ||
models [count.lisp](./count.lisp) and [addition.lisp](addition.lisp) are based on | ||
the [ACT-R tutorial](http://act-r.psy.cmu.edu/software/). | ||
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The scripts [count.py](count.py) and [addition.py](addition.py) can be run | ||
to create the MDF .json and .yaml files for the given example and execute it | ||
using the MDF scheduler. | ||
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The below graph represents the basic structure of all ACT-R models in MDF: | ||
![actr.png](actr.png) | ||
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There are also more detailed graphs [count.png](count.png) and | ||
[addition.png](addition.png) for each example. | ||
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### Count Model | ||
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[ACT-R](count.lisp) | [JSON](count.json) | [YAML](count.yaml) | [Python Script](count.py) | [Graph](count.png) | ||
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![count.png](count.png) | ||
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The count model counts from a start value to an end value. The script | ||
[count.py](count.py) first reads the original ACT-R model file | ||
[count.lisp](count.lisp), generates an MDF representation using the MDF ACT-R interface, and outputs the [JSON](count.json) and [YAML](count.yaml) files. It then executes | ||
the MDF model using the MDF scheduler and outputs the final goal set by the | ||
model once execution is finished. The final goal has the form: | ||
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`{'name': 'first-goal', 'ISA': 'count-from', 'start': 'two', 'end': 'four', 'count': 'four'}` | ||
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In this example, the start value is two, the end value is four, and the final | ||
value of count is four, indicating that the model counted from two to four. The | ||
start and end values can be modified in line 17 of [count.lisp](count.lisp), | ||
which sets the initial goal of the model: | ||
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`(first-goal ISA count-from start two end four)` | ||
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The script can use any values specified in [count.lisp](count.lisp), so the | ||
model can be modified and run multiple times in order to test different values. | ||
The [count](count.png) graph represents this example. | ||
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### Addition Model | ||
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[ACT-R](addition.lisp) | [JSON](addition.json) | [YAML](addition.yaml) | [Python Script](addition.py) | [Graph](addition.png) | ||
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![addition.png](addition.png) | ||
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The addition model computes the sum of two numbers. The script | ||
[addition.py](addition.py) functions identically to the previous example, but | ||
uses the addition model instead. The final goal has the form: | ||
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`{'name': 'second-goal', 'ISA': 'add', 'arg1': 'five', 'arg2': 'two', 'sum': 'seven', 'count': 'nil'}` | ||
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In this case, the first argument is five, the second argument is two, and the | ||
model has calculated the sum, seven. Like the previous example, the arguments | ||
can be modified in line 23 of [addition.lisp](addition.lisp) in order to test | ||
different values: | ||
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`(second-goal ISA add arg1 five arg2 two)` | ||
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The [addition](addition.png) graph represents this example. |
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