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Planning Domain Definition Language
The Planning Domain Definition Language (PDDL) is an attempt to standardize Artificial Intelligence (AI) planning languages. It was first developed by Drew McDermott and his colleagues in 1998 mainly to make the 1998/2000 International Planning Competition (IPC) possible, and then evolved with each competition. The standardization provided by PDDL has the benefit of making research more reusable and easily comparable, though at the cost of some expressive power, compared to domain-specific systems.
PDDL is a human-readable format for problems in automated planning that gives a description of the possible states of the world, a description of the set of possible actions, a specific initial state of the world, and a specific set of desired goals. Action descriptions include the prerequisites of the action and the effects of the action. PDDL separates the model of the planning problem into two major parts:
The problem description includes the initial state and the goals to be accomplished. The example below gives a domain definition and a problem description instance for the automated planning of a robot with two gripper arms.
PDDL becomes the input to planner software, which is usually a domain-independent Artificial Intelligence (AI) planner. PDDL does not describe the output of the planner software, but the output is usually a totally or partially ordered plan, which is a sequence of actions, some of which may be executed in parallel.
The PDDL language was inspired by the Stanford Research Institute Problem Solver (STRIPS) and the Action description language (ADL), among others. The PDDL language uses principles from knowledge representation languages which are used to author ontologies, an example is the Web Ontology Language (OWL). Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects. The PDDL language also uses principles from Object-oriented programming (OOP), in that the domain description has an object-type hierarchy that is relatively fixed like the class-hierarchy in OOP, and the problem description varies from one planning problem to another like the data in instances of an object.
The latest version of PDDL is described in a BNF (Backus–Naur Form) syntax definition of PDDL 3.1. Several online resources of how to use PDDL are available, and also a book.
This was the official language of the 1st and 2nd IPC in 1998 and 2000 respectively. It separated the model of the planning problem in two major parts:
Such a division of the model allows for an intuitive separation of those elements, which are (1) present in every specific problem of the problem-domain (these elements are contained in the domain-description), and those elements, which (2) determine the specific planning-problem (these elements are contained in the problem-description). Thus several problem-descriptions may be connected to the same domain-description (just as several instances may exist of a class in OOP (Object Oriented Programming) or in OWL (Web Ontology Language) for example). Thus a domain and a connecting problem description forms the PDDL-model of a planning-problem, and eventually this is the input of a planner (usually domain-independent AI planner) software, which aims to solve the given planning-problem via some appropriate planning algorithm. The output of the planner is not specified by PDDL, but it is usually a totally or partially ordered plan (a sequence of actions, some of which may be executed even in parallel sometimes). Now lets take a look at the contents of a PDDL1.2 domain and problem description in general.
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Planning Domain Definition Language AI simulator
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Planning Domain Definition Language
The Planning Domain Definition Language (PDDL) is an attempt to standardize Artificial Intelligence (AI) planning languages. It was first developed by Drew McDermott and his colleagues in 1998 mainly to make the 1998/2000 International Planning Competition (IPC) possible, and then evolved with each competition. The standardization provided by PDDL has the benefit of making research more reusable and easily comparable, though at the cost of some expressive power, compared to domain-specific systems.
PDDL is a human-readable format for problems in automated planning that gives a description of the possible states of the world, a description of the set of possible actions, a specific initial state of the world, and a specific set of desired goals. Action descriptions include the prerequisites of the action and the effects of the action. PDDL separates the model of the planning problem into two major parts:
The problem description includes the initial state and the goals to be accomplished. The example below gives a domain definition and a problem description instance for the automated planning of a robot with two gripper arms.
PDDL becomes the input to planner software, which is usually a domain-independent Artificial Intelligence (AI) planner. PDDL does not describe the output of the planner software, but the output is usually a totally or partially ordered plan, which is a sequence of actions, some of which may be executed in parallel.
The PDDL language was inspired by the Stanford Research Institute Problem Solver (STRIPS) and the Action description language (ADL), among others. The PDDL language uses principles from knowledge representation languages which are used to author ontologies, an example is the Web Ontology Language (OWL). Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects. The PDDL language also uses principles from Object-oriented programming (OOP), in that the domain description has an object-type hierarchy that is relatively fixed like the class-hierarchy in OOP, and the problem description varies from one planning problem to another like the data in instances of an object.
The latest version of PDDL is described in a BNF (Backus–Naur Form) syntax definition of PDDL 3.1. Several online resources of how to use PDDL are available, and also a book.
This was the official language of the 1st and 2nd IPC in 1998 and 2000 respectively. It separated the model of the planning problem in two major parts:
Such a division of the model allows for an intuitive separation of those elements, which are (1) present in every specific problem of the problem-domain (these elements are contained in the domain-description), and those elements, which (2) determine the specific planning-problem (these elements are contained in the problem-description). Thus several problem-descriptions may be connected to the same domain-description (just as several instances may exist of a class in OOP (Object Oriented Programming) or in OWL (Web Ontology Language) for example). Thus a domain and a connecting problem description forms the PDDL-model of a planning-problem, and eventually this is the input of a planner (usually domain-independent AI planner) software, which aims to solve the given planning-problem via some appropriate planning algorithm. The output of the planner is not specified by PDDL, but it is usually a totally or partially ordered plan (a sequence of actions, some of which may be executed even in parallel sometimes). Now lets take a look at the contents of a PDDL1.2 domain and problem description in general.