def manage_sb_compilation(phi, psi, m_atom, a, E):
if OPTIMIZED:
monitoring_atom = ds.Literal(m_atom, False)
added_precond = (None, False)
if can_make_true(a, phi):
R_phi = regression_aux(phi, a)
rho = ds.Or([R_phi.negate(), monitoring_atom]).simplified()
added_precond = (rho, True)
if can_make_true(a, psi):
R_psi = regression_aux(psi, a)
if R_psi != psi:
add_cond_eff(E, create_cond_eff(R_psi, monitoring_atom))
return added_precond
else:
monitoring_atom = ds.Literal(m_atom, False)
R_phi = regression_aux(phi, a)
if R_phi == phi:
added_precond = (None, False)
else:
rho = ds.Or([R_phi.negate(), monitoring_atom]).simplified()
added_precond = (rho, True)
R_psi = regression_aux(psi, a)
if R_psi != psi:
add_cond_eff(E, create_cond_eff(R_psi, monitoring_atom))
return added_precond
def simple_substitution(set, phi):
if phi == ds.TRUE():
return ds.TRUE()
if phi == ds.FALSE():
return ds.FALSE()
if isinstance(phi, ds.Literal):
if phi in set:
return ds.TRUE()
phi_neg = phi.negate()
if phi_neg in set:
return ds.FALSE()
else:
# phi is not in the initial state
if phi.negated:
return ds.TRUE()
else:
return ds.FALSE()
elif isinstance(phi, ds.Or):
return ds.Or([
simple_substitution(set, component) for component in phi.components
])
else:
# Conjunction
return ds.And([
simple_substitution(set, component) for component in phi.components
])
def gamma(literal, action):
disjunction = []
for cond, eff in get_effects(action):
if literal == eff:
if cond == ds.TRUE():
# in this case cond == TRUE(), so it is a simple effect
return ds.TRUE()
# cond is a list of literals
disjunction.append(cond)
if not disjunction:
return ds.FALSE()
return ds.Or(disjunction)
def convert_formula(formula):
if isinstance(formula, pddl.Atom):
return ds.Literal(atom_str(formula), False)
elif isinstance(formula, pddl.NegatedAtom):
return ds.Literal(atom_str(formula), True)
elif isinstance(formula, pddl.Disjunction):
return ds.Or([convert_formula(part) for part in formula.parts])
elif isinstance(formula, pddl.Conjunction):
return ds.And([convert_formula(part) for part in formula.parts])
elif isinstance(formula, pddl.Truth):
return ds.TRUE()
elif isinstance(formula, pddl.Falsity):
return ds.FALSE()
def regression(phi, action):
if isinstance(phi, ds.TRUE):
return phi
elif isinstance(phi, ds.FALSE):
return phi
elif isinstance(phi, ds.Literal):
return gamma_substitution(phi, action)
elif isinstance(phi, ds.Or):
return ds.Or(
[regression(component, action) for component in phi.components])
else:
# And
return ds.And(
[regression(component, action) for component in phi.components])
def manage_amo_compilation(phi, m_atom, a, E):
if OPTIMIZED:
if not can_make_true(a, phi):
return None, False
monitoring_atom = ds.Literal(m_atom, False)
R = regression_aux(phi, a)
if R == phi:
return None, False
else:
rho = ds.Or([R.negate(), monitoring_atom.negate(),
phi]).simplified()
add_cond_eff(E, create_cond_eff(R, monitoring_atom))
return rho, True
else:
monitoring_atom = ds.Literal(m_atom, False)
R = regression_aux(phi, a)
if R == phi:
return None, False
else:
rho = ds.Or([R.negate(), monitoring_atom.negate(),
phi]).simplified()
add_cond_eff(E, create_cond_eff(R, monitoring_atom))
return rho, True
def gamma_substitution(literal, action):
negated_literal = literal.negate()
gamma1 = gamma(literal, action)
gamma2 = gamma(negated_literal, action).negate()
conjunction = ds.And([literal, gamma2])
return ds.Or([gamma1, conjunction])