This package contains interface definitions for probabilistic reasoning with ROS services. A common type for describing variables is a json serialized dictionary mapping from variables from values. This datatype is inspired from jpt.variables.VariableAssignment and further documentation can be read in the jpts documentation (TODO reference to online hosted documentation).
An example for such a datastructure is
{"symbolic": "A", "integer": [1, 2, 3], "numeric": [0., 0.5]}
describes that the variable named symbolic has the value A,
the variable named integer has the value 1, 2 or 3 and the variable named numeric is in the range of 0 to 0.5.
asks the model for the most probable explanation
Defined inference types are
- Marginal probabilities and conditional probabilities via the infer service. (srv/infer.srv)
- Most probable explanations (modes) via the mpe service. (srv/mpe.srv)
- Sampling from the MPE state. (srv/sample_mpe.srv)
- Applying "permanent" evidence to the model. (srv/apply_evidence.srv)
Further information about the limits probabilistic inference is found in http://starai.cs.ucla.edu/papers/ProbCirc20.pdf.