Action description language

In artificial intelligence, Action description language (ADL) is an automated planning and scheduling system in particular for robots. It is considered an advancement of STRIPS. Pednault proposed this language in 1987.

Pednault observed that the expressive power of STRIPS was susceptible of being improved by allowing the effects of an operator to be conditional. This is the main idea of ADL-A, which is basically the propositional fragment of the ADL proposed by Pednault . ADL-B is an extension of A. In this extension, actions can be described with indirect effects by the introduction of a new kind of propositions, ”static laws. A third variation of ADL is ADL-C. This last one is similar to B, in the sense that its propositions can be classified into static and dynamic laws. But there are some more particularities

The sense of a planning language is to represent certain conditions in the environment, and, based on these, automatically generate a chain of actions which lead to a desired goal. A goal is a certain partially specified condition. Before an action can be executed, its preconditions must be fulfilled; after the execution, the action yields effects, by which the environment changes. The environment is described by means of certain predicates, which are either fulfilled or not.

Contrary to STRIPS, the principle of the open world applies with ADL: everything not occurring in the conditions is unknown (Instead of being assumed false). In addition, while in STRIPS only positive literals and conjunctions are permitted, ADL allows negative literals and disjunctions as well.

Complexity of planning

In terms of computational efficiency, ADL can be located between STRIPS and the situation calculus . Any ADL problem can be translated into a STRIPS instance. However, existing compilation techniques are worst-case exponential . This wort case cannot be improved if we are willing to preserve the length of plans polynomially , and thus ADL is strictly more brief than STRIPS.

ADL planning, however, is still a PSPACE-complete problem.Most of the algorithms polynomial space even if the preconditions and effects are complex formulae .

Interestingly, most of the top-performing approaches to classical planning internally utilize a STRIPS like representation. In fact, most of the planners (FF, LPG, Fast-Downward, SGPLAN5 , LAMA) first translate the ADL instance into one that is essentially a STRIPS one (without conditional or quantified effects or goals).

Example

Consider the problem of air freight transport, where certain goods must be transported from an airport to another airport by plane and where airplanes need to be loaded and unloaded.

The necessary actions would be loading, unloading and flying; over the descriptors one could express In(c, p) and At(x, a) whether a freight C is in an airplane p and whether an object x is at an airport A.

The actions could be defined then as follows:

Action (
  Load (c: Freight, p: Airplane, A: Airport)
  Precondition: At(c, A) ^ At(p, A)
  Effect: ¬At(c, A) ^ In(c, p)
)

Action (
  Unload (c: Freight, p: Airplane, A: Airport)
  Precondition: In(c, p) ^ At(p, A) 
  Effect: At(c, A) ^ ¬In(c, p)
)

Action (
  Fly (p: Airplane, from: Airport, to: Airport)
  Precondition: At(p, from)
  Effect: ¬At(p, from) ^ At(p, to)
)

See also

• Automated planning
• Hierarchical task network
• Planning Domain Definition Language (PDDL)
• STRIPS