cram planning algorithm

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I could find some related answers to the above questions from Prof. Moritz Tenorth's paper below. Thus, Cram has HTN like planning algorithm but not the same. Also, Cram has CPL which is a bit different from PDDL. I would like to know more in detail about the differences of cram from standards. Also, I am still curious to know how the action planning and semantic KB are interacting each other.

"II. RELATED WORK As a platform for knowledge exchange between heterogeneous robots, ROBOEARTH requires very expressive and highly semantic representations that provide a robot with all information it needs to select information from the knowledge base, adapt it, and reason about its applicability. Earlier research on knowledge representation for actions or objects usually did not deal with this kind of meta-information needed for autonomously exchanging knowledge. Hierarchical Task Networks (HTN [4]) and related languages for plan representation [19] or workflow specification [18] are similar to the action representation used in ROBOEARTH, but focus on the description of the task itself, i.e., its subactions, goals, and ordering constraints. The Planning Domain Definition Language (PDDL) [20] follows a different approach describing actions as first principles from which plans are constructed during run-time using AI planning techniques. XABSL [5], mainly used in the RoboCup soccer context, describes actions in terms of hierarchical finite state machines. AutomationML [6] is a standard for describing task information and spatial configurations, mainly used in industrial applications. The FIPA [7] standard primarily deals with the definition of communication standards for software agents. Object description formats like the proprietary DXF [8] or the open Collada [9] standard describe objects by their meshes and textures, but without further specifying semantic properties. The Knowledge Interchange Format (KIF) [21] is a very expressive generic exchange language that aims at a self-contained representation. The high expressiveness however comes at the cost of limited reasoning support. For ROBOEARTH, we chose a shared ontology as pragmatic solution. We are not aware of any other system that integrates task descriptions, spatial information, semantic information about object types and meta-information about the exchanged data in a common language supporting abstract reasoning. Related work on sharing knowledge among robots focused either on sharing a common belief state in multirobot systems [10], or on fundamental aspects like how heterogeneous robots can autonomously acquire and share symbols created from perceptual cues [11]. Our interest is rather on creating a system for exchanging complex manipulation task-related information, so we simplify some of these aspects by assuming that a common base ontology is shared by all parties and that perception is done using the provided object models which are linked to the classes in the ontology."

Quoted from the paper Moritz Tenorth, et.al, Representation and Exchange of Knowledge About Actions, Objects, and Environments in the ROBOEARTH Framework IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 1