How can the logic programming paradigm handle non-determinism?

3 Choice points and cuts

Another way to handle non-determinism in logic programming is to use choice points and cuts. Choice points are points in a logic program where more than one alternative is available, such as a rule with multiple clauses, or a disjunction. Cuts are special symbols that can be used to prune the computation tree and eliminate unwanted alternatives. For example, in Prolog, the symbol ! is a cut that commits to the current choice and prevents backtracking. Cuts can be used to improve performance, control non-determinism, or enforce determinism.

4 Constraint logic programming

A third way to handle non-determinism in logic programming is to use constraint logic programming. Constraint logic programming is an extension of logic programming that allows the use of constraints, or conditions that limit the possible values of variables. Constraints can be used to express complex relations, such as arithmetic, logical, or temporal constraints, and to solve problems, such as scheduling, optimization, or satisfiability. Constraint logic programming can handle non-determinism by using constraint propagation, or the process of reducing the domains of variables based on the constraints, and constraint solving, or the process of finding consistent assignments of values to variables that satisfy the constraints.

5 Probabilistic logic programming

A fourth way to handle non-determinism in logic programming is to use probabilistic logic programming. Probabilistic logic programming is an extension of logic programming that allows the use of probabilities, or measures of uncertainty or likelihood, in logic programs. Probabilities can be used to express uncertain facts, rules, or goals, and to reason about stochastic phenomena, such as randomness, noise, or incomplete information. Probabilistic logic programming can handle non-determinism by using probabilistic inference, or the process of computing the probability of a query given the evidence, and probabilistic learning, or the process of learning the parameters or structure of a probabilistic logic program from data.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?