aima/logic.py
def dpll_satisfiable(s):
    """Check satisfiability of a propositional sentence.
    This differs from the book code in two ways: (1) it returns a model
    rather than True when it succeeds; this is more useful. (2) The
    function find_pure_symbol is passed a list of unknown clauses, rather
    than a list of all clauses and the model; this is more efficient.
    >>> ppsubst(dpll_satisfiable(A&~B))
    {A: True, B: False}
    >>> dpll_satisfiable(P&~P)
    False
    """
    clauses = conjuncts(to_cnf(s))
    symbols = prop_symbols(s)
    return dpll(clauses, symbols, {})
def dpll_satisfiable(s):
    """Check satisfiability of a propositional sentence.
    This differs from the book code in two ways: (1) it returns a model
    rather than True when it succeeds; this is more useful. (2) The
    function find_pure_symbol is passed a list of unknown clauses, rather
    than a list of all clauses and the model; this is more efficient.
    >>> ppsubst(dpll_satisfiable(A&~B))
    {A: True, B: False}
    >>> dpll_satisfiable(P&~P)
    False
    """
    clauses = conjuncts(to_cnf(s))
    symbols = prop_symbols(s)
    return dpll(clauses, symbols, {})
Check satisfiability of a propositional sentence.
    This differs from the book code in two ways: (1) it returns a model
    rather than True when it succeeds; this is more useful. (2) The
    function find_pure_symbol is passed a list of unknown clauses, rather
    than a list of all clauses and the model; this is more efficient.
    >>> ppsubst(dpll_satisfiable(A&~B))
    {A: True, B: False}
    >>> dpll_satisfiable(P&~P)
    False