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Project-Team: MODBIO

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Project-Team : modbio

Section: Scientific Foundations

Constraint programming

Constraint programming [50] is a declarative programming language paradigm that appeared in the late 80's, and which has become more and more popular since then. A constraint is a logical formula that defines a relation to be satisfied by the values of the variables the formula contains. For instance, the formula x + y$ \le$1 expresses that the sum of the values of the variables x and y must be less than or equal to 1.

In constraint programming, the user programs with constraints, i.e., he or she describes a problem by a set of constraints, which are connected by combinators such as conjunction, disjunction, or temporal operators (always). Each constraint gives some partial information about the state of the system to be studied. Constraint programming systems allow one to deduce new constraints from the given ones and to compute solutions, i.e., values for the variables that satisfy all constraints simultaneously.

One of the main goals of constraint programming is to develop programming languages that allow one to express constraint problems in a natural way, and to solve them efficiently.

Finite domain constraint programming

In our work, we are first interested in constraint problems over finite domains. In this case, the domain of each variable (the set of values it may take) is a finite set of integer numbers. Theory tells us that most constraint problems over finite domains are NP-hard, which means that there is little hope to solve them by algorithms polynomial in the size of the input. In practice, these problems are handled by tree search methods which try successively different valuations of the variables until a solution is found. Because of the exponential number of possible combinations, it is crucial to reduce the search space as much as possible, i.e., to eliminate a priori as many valuations as possible.

There exist two generic methods to solve such problems. The first one is classical integer linear programming (see also Sect.  3.3), which has been studied in mathematical programming and operations research for more than 40 years. Here, constraints are linear equations and inequalities over the integer numbers. In order to reduce the search space, one typically uses the linear relaxation of the constraint set. Equations and inequalities are first solved over the real numbers, which is much easier; then the information obtained is used to prune the search tree.

The second method is finite domain constraint programming which arose in the last 15 years by combining ideas from declarative programming languages and constraint satisfaction techniques in artificial intelligence. In contrast to integer linear optimization one uses, in addition to simple arithmetic constraints, more complex constraints, which are called symbolic constraints. For instance, the symbolic constraint alldifferent(x1, ..., xn) expresses that the values of the variables x1, ..., xn must be pairwise distinct. Such a constraint is difficult to express in a compact way using only linear equations and inequalities. Symbolic constraints are handled individually by specific filtering algorithms that reduce the domain of the variables. This information is propagated to other constraints which may further reduce the domains.

A state-of-the-art survey of finite domain constraint programming, with special emphasis on its relation to integer linear programming can be found in [12], see also Sect.  6.9.

Concurrent constraint programming

In concurrent constraint programming (cc) [44], different computation processes may run concurrently. Interaction is possible via the constraint store. The store contains all the constraints currently known about the system. A process may tell the store a new constraint, or ask the store whether some constraint is entailed by the information currently available, in which case further action is taken.

Hybrid concurrent constraint programming (Hybrid cc) [38] is an extension of concurrent constraint programming which allows one to model and to simulate the temporal evolution of hybrid systems, i.e., systems that exhibit both discrete and continuous state changes. Constraints in Hybrid cc may be both algebraic and differential equations. State changes can be specified using the combinators of concurrent constraint programming and default logic. Hybrid cc is well-suited to model dynamic biological systems, as shown in [4].