  Autonomy in robot-assisted surgery is essential to reduce surgeons' cognitive
load and eventually improve the overall surgical outcome. A key requirement for
autonomy in a safety-critical scenario as surgery lies in the generation of
interpretable plans that rely on expert knowledge. Moreover, the Autonomous
Robotic Surgical System (ARSS) must be able to reason on the dynamic and
unpredictable anatomical environment, and quickly adapt the surgical plan in
case of unexpected situations. In this paper, we present a modular Framework
for Robot-Assisted Surgery (FRAS) in deformable anatomical environments. Our
framework integrates a logic module for task-level interpretable reasoning, a
biomechanical simulation that complements data from real sensors, and a
situation awareness module for context interpretation. The framework
performance is evaluated on simulated soft tissue retraction, a common surgical
task to remove the tissue hiding a region of interest. Results show that the
framework has the adaptability required to successfully accomplish the task,
handling dynamic environmental conditions and possible failures, while
guaranteeing the computational efficiency required in a real surgical scenario.
The framework is made publicly available.
