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AI (artificial intelligence) planning is an important branch of artificial intelligence. Planning decomposition is an important topic of intelligent planning research, which plays a key role in improving planning speed and reducing the scale of planning expansion. The decomposition algorithm in intelligent planning is studied extensively and deeply, and the development history is introduced comprehensively. This paper expounds and analyzes the general forms of planning decomposition, and classifies the planning decomposition from various situations, in which key methods and popular applications of planning decomposition are mainly introduced. The main contents and advantages of the decomposition method are introduced from the aspects of traditional methods, abstraction levels, constraint satisfaction problems, sub-objective ordering, etc. Application areas include improvements in planning algorithms, multi-agent systems, software test-case generation, large Markov decision processes, etc. This paper summarizes the problems and deficiencies of the existing planning decomposition, and analyzes the future direction.

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One of the challenges to build a smart city is to create intelligent and ubiquitous geospatial Web services, and provide a composition of these services available to users. These geospatial Web services must be better-tuned to a given context. Using AI planning techniques and semantic enhancement, this paper presents a dynamic, context-aware service composition method which is achieved by transforming the service composition problem into a planning problem described in a standardized fashion using PDDL. Semantic representation of a geospatial Web service is modeled by extending OWL-S ontology with GeoContext class, GeoContextPrecondition class, GeoContextEffect class and GeoContextBinding class, which support geo-context and geo-context adaptation. Semantic information is used for the enhancement of the composition process as well as for approximating the optimal composite service when exact solutions are not found. Independence from specific planners is maintained. The generating plan is transformed to a WS-BPEL compatible representation, which is executable on the business process execution engine. A case study about smart travel is also presented to demonstrate the functionality, effectiveness and potential of the approach. [ABSTRACT FROM AUTHOR]