Dana Scott used the partial order among partial functions for his
mathematical model of recursively defined functions. He interpreted the partial
order as one of information content. In this paper we elaborate on Scott's
suggestion of regarding computation as a process of information maximization by
applying it to the solution of constraint satisfaction problems. Here the
method of constraint propagation can be interpreted as decreasing uncertainty
about the solution -- that is, as gain in information about the solution.
The CLP scheme uses Horn clauses and SLD resolution to generate multiple
constraint satisfaction problems (CSPs). The possible CSPs include rational
trees (giving Prolog) and numerical algorithms for solving linear equations and
linear programs (giving CLP(R)). In this paper we develop a form of CSP for
interval constraints. In this way one obtains a logic semantics for the
efficient floating-point hardware that is available on most computers.