In artificial intelligence, a fluent is a condition that can change over time. In logical approaches to reasoning about actions, fluents can be represented in first-order logic by predicates. This representation of fluents is modified in the situation calculus by using the sequence of the past actions in place of the current time.

is a function and not a predicate. In first-order logic, converting predicates to functions is called reification is the one that tells when fluents are true. This representation of fluents is used in the event calculus, in the fluent calculus, and in the features and fluents logics.

. Conditions that can be represented in this way are called functional fluents. Some fluents are represented this way in the situation calculus.

Naive physics

From a historical point of view, fluents were introduced in the context of qualitative reasoning. The idea is to describe a process model not with mathematical equations but with natural language. That means an action is not only determined by its trajectory, but with a symbolic model, very similar to a text adventure. Naive physics stands in opposition to a numerical physics engine and has the obligation to predict the outcome of actions. The fluent realizes the common sense grounding between the robot's motion and the task description in natural language.

From a technical perspective, a fluent is equal to a parameter that is parsed by the naive physics engine. The parser converts between fluents and numerical values measured by sensors.

See also

• Event calculus

• Fluent calculus

• Frame problem

• Situation calculus

References