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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/8522
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dc.contributor.authorK., Pradheep Kumar-
dc.date.accessioned2023-01-17T10:17:09Z-
dc.date.available2023-01-17T10:17:09Z-
dc.date.issued2019-
dc.identifier.urihttps://www.igi-global.com/chapter/modified-backward-chaining-algorithm-using-artificial-intelligence-planning-iot-applications/232006-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8522-
dc.description.abstractIn this chapter, an automated planning algorithm has been proposed for IoT-based applications. A plan is a sequence of activities that leads to a goal or sub-goals. The sequence of sub-goals leads to a particular goal. The plans can be formulated using forward chaining where actions lead to goals or by backward chaining where goals lead to actions. Another method of planning is called partial order planning where all actions and sub-goals are not illustrated in the plan and left incomplete. When many IoT devices are interconnected, based on the tasks and activities involved resource allocation has to be optimized. An optimal plan is one where the total plan length is minimum, and all actions consume similar quantum of resources to achieve a goal. The scheduling cost incurred by way of resource allocation would be minimum. Compared to the existing algorithms L2-Plan (Learn to Plan) and API, the algorithm developed in this work improves optimality of resources by 14% and 36%, respectivelyen_US
dc.language.isoenen_US
dc.publisherIGI Globalen_US
dc.subjectComputer Scienceen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectIoT Applicationsen_US
dc.subjectAlgorithmen_US
dc.titleModified Backward Chaining Algorithm Using Artificial Intelligence Planning IoT Applicationsen_US
dc.typeArticleen_US
Appears in Collections:Department of Computer Science and Information Systems

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