The Knowledge Systems Lab (KSL) at the University of Michigan created GMOV. It is stricly an experimental application meant to show how metadata that make digital objects FAIR can be used in a "Model Player."
The General Model Operator View (GMOV) is a prototype environment for loading, inspecting, and executing computational capabilities exposed through FAIR Digital Objects (FDOs). GMOV demonstrates how computational models packaged as FDOs can be made available as agent-usable Services within a Model–View–Controller (MVC) architecture.
The project explores a simple but powerful idea: rather than allowing AI agents to perform arbitrary computations directly, agents can be constrained to invoke trusted computational Services exposed by loaded FDOs. This approach supports explicit model selection, computational provenance, and transparent execution.
GMOV is intended as a research and demonstration platform for FAIR Digital Objects, Computable Biomedical Knowledge (CBK), agent-tool interaction, and provenance-preserving AI-enabled computation.
GMOV loads FDOs that contain one or more computational Models together with metadata describing:
- Identity
- Provenance
- Version
- Inputs
- Outputs
- Services
- Validation assets
- Documentation
In GMOV, a Model is a computational artifact loaded from an FDO.
Examples include:
- Phenotype determination models
- Clinical recommendation models
- Risk calculators
- Computational workflows
- Orchestration models
Models expose executable Services.
Services represent the callable computational capabilities made available to users and agents.
Examples:
- CYP2D6 phenotype lookup
- Codeine recommendation generation
- Tramadol recommendation generation
GMOV includes an Agent interface capable of receiving natural-language requests.
The Agent does not perform computations directly. Instead, it:
- Interprets user requests
- Identifies candidate Services
- Selects an appropriate Service
- Invokes the Controller
- Returns results and provenance
The Controller mediates between user requests and available Services.
Responsibilities include:
- Service discovery
- Service selection
- Input validation
- Service execution
- Provenance capture
GMOV follows the Model–View–Controller pattern.
Loaded FDOs and their executable Services.
The GMOV user interface.
The execution and routing layer that mediates between user requests and available Services.
This architecture makes it possible to constrain agent behavior to loaded computational capabilities while preserving transparency and reproducibility.
Load individual FAIR Digital Objects into the environment.
Model Assemblies may reference additional FDOs and Knowledge Sets.
GMOV resolves these dependencies and loads referenced Models when available.
Execute Services directly through the user interface.
Submit natural-language requests to the Agent.
The Agent selects and invokes available Services rather than generating computational results independently.
GMOV displays:
- Services considered
- Services rejected
- Selected Service
- Validation status
- Execution status
Results include provenance describing:
- Model used
- Service used
- Execution context
If an FDO contains a top-level index.html, GMOV exposes an Info link that opens the associated documentation.
GMOV supports:
- Unloading individual Models
- Unloading Model Assemblies
- Unloading all loaded Models
Load one or more FDOs into GMOV.
Review Services exposed by the loaded Models.
Run Services directly or invoke them through the Agent interface.
Inspect execution results and provenance.
Remove Models or Assemblies from the environment when no longer needed.
User request:
What is the recommendation for codeine for a poor CYP2D6 metabolizer?
Agent behavior:
Identify candidate Services
Select codeine recommendation Service
Validate phenotype input
Invoke Controller
Execute Service
Return result
Result:
Selected Service:
CPIC Codeine Recommendation
Result:
Avoid codeine because reduced conversion to morphine may reduce efficacy.
Provenance:
Model: CPIC Codeine Recommendation CYP2D6
Service: Codeine Recommendation
GMOV is intended to demonstrate:
- FAIR Digital Objects as computational components
- Models as agent-usable tools
- Constrained AI-enabled computation
- Explicit Service selection
- Computational provenance
- MVC-based orchestration of models and agents
GMOV is an experimental research prototype.
The project is intended to support demonstrations, architectural exploration, and future research into AI-enabled interaction with FAIR Digital Objects and computable knowledge resources.
It should not be considered production software.