Translation of artificial intelligence representations

#The subject disclosure relates to artificial intelligence representations, and more specifically, to translation of a graphical representation of domain knowledge associated with an artificial intelligence planning problem.

#Automated planning and scheduling is a branch of artificial intelligence (AI) that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots, and unmanned vehicles. Unlike classical control and classification problems, solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory. Planning may be performed such that solutions may be found and evaluated prior to execution; however, any derived solution often needs to be revised. Solutions usually resort to iterative trial and error processes commonly seen in artificial intelligence. These include dynamic programming, reinforcement learning, and combinatorial optimization.

#A planning problem generally comprises the following main elements: a finite set of facts, the initial state (a set of facts that are true initially), a finite set of action operators (with precondition and effects), and a goal condition. An action operator maps a state into another state. In the classical planning, the objective is to find a sequence of action operators (or planning action) that, when applied to the initial state, will produce a state that satisfies the goal condition. This sequence of action operators is called a plan.

#FIG. 1 illustrates a diagram of an example, non-limiting graphical representation of domain knowledge associated with an artificial intelligence planning problem in accordance with one or more embodiments of the disclosed subject matter.

#FIG. 2 illustrates a block diagram of an example, non-limiting system that can translate graphical representations of domain knowledge associated with an artificial intelligence planning problem in accordance with one or more embodiments of the disclosed subject matter.

#FIG. 3 illustrates a flowchart of an example, non-limiting computer-implemented method of translating graphical representations of domain knowledge associated with an artificial intelligence planning problem in accordance with one or more embodiments of the disclosed subject matter.

#The following detailed description is merely illustrative and is not intended to limit embodiments and/or application or uses of embodiments. Furthermore, there is no intention to be bound by any expressed or implied information presented in the preceding Background or Summary sections, or in the Detailed Description section.

#This disclosure relates in some embodiments to artificial intelligence representations, and more specifically, to translation of a graphical representation of domain knowledge associated with an artificial intelligence planning problem.

#Mind maps are examples of diagrams or graphical representations that can represent domain knowledge. These graphical representations can represent domain concepts, and relationships to other concepts, in a simple form. While it is often challenging to encode this domain knowledge in a planning language or artificial intelligence description language, it can be easier to express the domain knowledge graphically in a diagram. For example, and according to one or more embodiments of this disclosure, a domain expert having little or no knowledge of a particular artificial intelligence description language can still accurately express domain knowledge in a graphical representation. Furthermore, according to one or more embodiments of this disclosure, the graphical representation can be automatically translated into any particular artificial intelligence description language. Accordingly, embodiments of this disclosure provide artificial intelligence planning solutions where domain experts are not required to have specialized artificial intelligence training.