A reactive, probabilistic logic programming language using Reactive Circuits.

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  • Author: Simon Kohaut
  • Tags probabilistic logic programming , reactive circuits , answer set programming , inference
  • Requires: Python >=3.9
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Project description

Resin — Reactive Signal Inference

Resin is a probabilistic logic programming language for building reactive inference pipelines over continuous, asynchronous data streams. Resin programs are compiled via Answer Set Programming (ASP) into Reactive Circuits: vectorised, self-adapting computation graphs that perform Weighted Model Counting (WMC) in real time.

The core library is written in Rust. A Python package (pyresin) is published to PyPI and built with Maturin.

The Resin language

A Resin program declares sources (incoming signals), rules (first-order logic), and targets (the quantities to infer).

Source types

Type Declared with ASP encoding
Probability a value in [0, 1] choice atom {name}.
Boolean true/false choice atom {name}.
Density a continuous distribution one choice per comparison threshold
Number a scalar value one choice per comparison threshold

Syntax

Here is an example Resin program for an autonomous aircraft system navigating an urban environment.

# Source declarations
over(park)         <- source("/map/over/park", Probability).
distance(hospital) <- source("/map/distance/hospital", Density).
distance(airport)  <- source("/map/distance/airport", Density).
speed              <- source("/sensor/speed", Number).
flight_hours(w1)   <- source("/metrics/flight_hours/wing_1", Number).
flight_hours(w2)   <- source("/metrics/flight_hours/wing_2", Number).
flight_hours(w3)   <- source("/metrics/flight_hours/wing_3", Number).
flight_hours(w4)   <- source("/metrics/flight_hours/wing_4", Number).

# Propositional rules
permitted if over(park) and speed < 25.

# First-order rules
critical_infrastructure(hospital).
critical_infrastructure(airport).
safety_distance(T) if critical_infrastructure(T) and distance(T) > 100.

# Conditional probabilities and Noisy-OR over first-order instantiations
wing(w1). wing(w2). wing(w3). wing(w4).
needs_checkup(W) <- P(0.9) if flight_hours(W) > 100 and wing(W).
any_wing_needs_checkup if needs_checkup(W).

# Target that the program will be constrained on
safe if permitted and safety_distance(T) and not any_wing_needs_checkup.
safe -> target("/output/safe").

Rules supports variables (uppercase arguments, in the example above W, T) and conjunctions (and); disjunctions are implemented through multiple clauses. Comparison literals (<, >) on Number and Density sources are automatically mapped to the respective leaves, which hold time-dependent truth values or CDF/SF probabilities.

Installation

For employing Resin with Python, you can install the pre-compiled package via PyPI:

pip install pyresin

Python API

Compiling a model

from resin import Resin

model = """
active <- source("/sensors/active", Boolean).
alarm  if active.
alarm  -> target("/output/alarm").
"""

resin = Resin.compile(model, value_size=1, verbose=False)

value_size sets the width of the internal value-space vector (e.g. number of particles or grid cells for vectorised evaluation). This is helpful for running the same Resin program for many problem instances in parallel.

Writing signals

Both make_writer(channel) and make_writer_for(atom) return a correctly typed writer for the declared source — the former looks up by IPC channel name, the latter by source atom name.

# Boolean source — by channel name
bool_writer = resin.make_writer("/sensors/active")
bool_writer.write(True, timestamp=None)

# Probability source — by atom name
prob_writer = resin.make_writer_for("over(park)")
prob_writer.write([0.73], timestamp=None)

# Density source — pass distribution name and parameters
# Every time parameters are written, the density function may change
density_writer = resin.make_writer("/map/distance/hospital")
density_writer.write("normal", [[25.0], [5.0]], timestamp=None)
# Supported distributions: "normal", "lognormal", "exponential", "uniform"

# Number source for scalar comparison
number_writer = resin.make_writer_for("speed")
number_writer.write([12.5], timestamp=None)

Reading outputs

rc = resin.get_reactive_circuit()
results = rc.update()          # returns {target_name: [probability, ...]}
print(results["/output/alarm"])

Reactive Circuit adaptation

The underlying circuit can adapt its structure in response to changing signal frequencies:

rc.adapt(bin_size=0.1, number_bins=10)

Alternatively, leaves can be lifted or dropped at runtime, meaning we may manually indicate that a leaf's value changes more or less often than others:

names = resin.get_names()
rc.lift_leaf(names.index("distance_vessel_gt_100"))
rc.drop_leaf(names.index("distance_vessel_gt_100"))

Visualisation

The following allows inspecting the internal inference structure combined of the Reactive Circuit and each of its Algebraic Circuits.

rc.to_combined_svg("circuit.svg")

Building from source

Requirements: Rust toolchain, Clingo, Python ≥ 3.9, Maturin.

macOS

brew install clingo
export CLINGO_LIBRARY_PATH=$(brew --prefix clingo)/lib
maturin develop --release  # Optional for building the Python package

Linux

pip install clingo
CLINGO_DIR=$(python3 -c "import clingo, os; print(os.path.dirname(clingo.__file__))")
export CLINGO_LIBRARY_PATH="$CLINGO_DIR"
maturin develop --release  # Optional for building the Python package

Run tests

cargo test

License

See LICENSE.md.

Citation

If you find our work useful, please consider citing the paper Reactive Knowledge Representation and Asynchronous Reasoning:

@article{kohaut2026reactive,
  title={Reactive Knowledge Representation and Asynchronous Reasoning},
  author={Kohaut, Simon and Flade, Benedict and Eggert, Julian and Kersting, Kristian and Dhami, Devendra Singh},
  journal={arXiv preprint arXiv:2602.05625},
  year={2026}
}

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  • Author: Simon Kohaut
  • Tags probabilistic logic programming , reactive circuits , answer set programming , inference
  • Requires: Python >=3.9
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