PDDL and other languages. Lee McCluskey Department of Computing and Mathematical Sciences, The University of Huddersfield. Background. Related to AI Planning there are several kinds of knowledge that is required declaratively: Domain/environment Planning heuristics Task (input)
Department of Computing and
The University of Huddersfield
Related to AI Planning there are several kinds of knowledge that is required declaratively:
The basic requirement in PDDL is :strips which indicates the underlying semantics of the language
worlds are considered as sets of situations (states), where each state is specified by stating a list of all predicates that are true. States are changed instantaneously into new states by actions which change the truth value of predicates. Actions have preconditions and effects under the default persistence assumption.. Etc
The semantics of PDDL v1.2 (used for 98 competition) are informal and appear to be distributed among:
The rule is that action definitions are not allowed to have effects that mention predicates that occur in the :implies field [RHS] of an axiom (p13)
An action definition must have an :effect or an :expansion but not both (p8)
Extensions different handling of numeric quantities, addition of durative actions
Left out HTN actions (apparently no-one had used them!)
BUT attempted to give a formal semantics to the
Fox and Long in the v2.1 manual describe it explicitly as one. Although not much discussed, PDDLv1.2 actually provides modelling features..
. One can imagine having planning services around the web one supplies the problems + domain model in extended-PDDL and invokes the planning service.
Service: analyses the domain model and configures a planner to solve the problems
Ontologies are explicit specifications of a conceptual model for sharing the understanding of a particular domain.
Some ontologies for planning concepts have been created e.g. PLANET (Blythe) and SPAR (Tate). They are deemed essential for on-line agent communication between agents involved in planning (but promise multiple benefits eg in the KA process).
ontologies need to be developed.
Timely maturing of 4 research areas
Can be combined to solve the biggest problems in AI planning currently lack of Accessibility and Usability of the technology