SH is a software system designed to solve complex planning problems in various domains. It is based on state-based Hierarchical Task Network (HTN) planning and relies on a version of the Hierarchical Planning Definition Language (HDPL) for specifying planning problems. What sets SH apart is its modular and service-oriented architecture, providing flexibility, extensibility, and maintainability. The SH planning system offers versatile capabilities, including modular parsing of planning problems, quick variable binding and predicate grounding on the fly during plan generation, resource-efficient plan generation, and seamless integration into other larger systems. These capabilities make SH suitable for addressing real-world challenges in planning, automation, assistance, and decision-making.
build.sbtClone the repository and build the project using sbt to prepare the codebase for use.
Client objectTo run a test case, use the PlanningServices.planWithGivenDomainAndProblemNamePrintPlans service within the Client object with the following parameters:
Ensure that the domain name and problem name match their corresponding filenames in the resources/repository/<domainName> directory. The generated plan, along with performance statistics, will be displayed in the terminal.
Execute the Client object with:
PlanningServices.planWithGivenDomainAndProblemNamePrintPlans("deployment", "p-dashboard", 1)Expected output:
Plan 1:
1) createinstance(dashboard)
2) createinstance(apache2)
3) start(1.0)
4) bind(httpd,0.0,1.0)
5) start(0.0)
6) createinstance(cassandra)
7) start(2.0)
8) run(2.0)
9) bind(cass-up,0.0,2.0)
10) run(0.0)
-------------------------------------------* Statistics *-------------------------------------------
Number of found plans: 1
First plan length: 10
*************************************************
Parsing time: 968.0 ms
Plan-generation time: 63.0 ms
Memory usage: 133.169152 MB
*************************************************
Number of evaluated states: 34
Number of decomposed tasks: 24
Number of recursions: 36
----------------------------------------------------------------------------------------------------Create and import a jar of the SH Planner System. Once imported, the following methods from the PlanningServices object are available for use:
planWithProvidedProblemInString(problemToBeSolved: String, numberOfPlans: Int = 1)storeProvidedDomainAndProblemInString(domainToBeSolved: String, problemToBeSolved: String)planWithProvidedDomainAndProblemInString( domainToBeSolved: String,problemToBeSolved: String, numberOfPlans: Int = 1)checkProvidedProblemInString(problemToBeChecked: String)checkProvidedDomainInString(domainToBeChecked: String)planWithGivenDomainAndProblemNameReturnString(domainName: String, problemName: String, numberOfPlans: Int = 1) Note: The domain and problem files need to be present in the resources/repository/<domainName> directory.
The SH Planning System can be deployed as a Web service. Start the Web server by running the HTTPServer object. By default, the server is available at 127.0.0.1:9090. Configure the host and port in application.conf as needed.
| Endpoint | Request Type | Body (Text) |
|---|---|---|
services/status |
GET | Check SH's status |
services/syntax-verification/domain |
POST | Verify a domain definition |
services/syntax-verification/problem |
POST | Verify a problem definition |
services/plan-generation/domain-and-problem |
POST | Plan with domain and problem definitions in string format |
services/plan-generation/problem-in-string |
POST | Plan with a problem definition in string format |
services/storing/domain-and-problem |
POST | Store HPDL domain and problem definitions |
Note:
services/planner/problem-in-string, the domain must be already available in the repository.TBD
Georgievski, I. Coordinating services embedded everywhere via hierarchical planning. Ph.D. Thesis, University of Groningen, October 2015.
Georgievski, I. and Aiello, M. HTN planning: Overview, comparison, and beyond. Artificial Intelligence, 222(0): 124-156. 2015.
Georgievski, I., Nguyen, T. A., Nizamic, F., Setz, B., Lazovik, A., and Aiello, M. Planning meets activity recognition: Service coordination for intelligent buildings. Pervasive and Mobile Computing, 38(1): 110–139. 2017.
Georgievski, I., Nizamic, F., Lazovik, A., and Aiello, M. Cloud Ready Applications Composed via HTN Planning. In IEEE International Conference on Service Oriented Computing and Applications, pages 23–33, 2017.