No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Scheduling for Flexible Package Production
Abstract
This paper includes part of the strategies used to solve a scheduling problem developed for a company that produces flexible packaging. In this problem it is necessary to schedule several jobs that involve four processes and for each there is a group of machines available with similar characteristics. Each activity is performed on just one machine. The scheduling must also try to verify certain conditions. There is a list of attributes for each process, and consequently for all the activities that perform this process.
... In this paper, we present a refinement of that work, which includes not only the capability of learning hierarchical plan libraries from unlabeled examples, but it also allows these examples to contain interleaved plans. In order to illustrate the proposed approach, a simple example from the flexible packaging industry domain described in [8], is given in this work. ...
Most of the available plan recognition techniques are based on the use of a plan library in order to infer users intentions and/or strategies. Until some years ago, plan libraries were completely hand coded by human experts, which is an expensive, error prone and slow process. Besides, plan recognition systems with hand-coded plan libraries are not easily portable to new domains, and the creation of plan libraries require not only a domain expert, but also a knowledge representation expert. These are the main reasons why the problem of automatic generation of plan libraries for plan recognition, has gained much importance in recent years. Even when there is considerable work related to the plan recognition process itself, less work has been done on the generation of such plan libraries. In this paper, we present an algorithm for learning hierarchical plan libraries from action sequences, based on a few simple assumptions and with little given domain knowledge, and we provide a novel mechanism for supporting interleaved plans in the input example cases.
... In this paper, we present a refinement of that work, which includes not only the capability of learning hierarchical plan libraries from unlabeled examples, but it also allows these examples to contain interleaved plans. In order to illustrate the proposed approach, a simple example from the flexible packaging industry domain described in [8], is given in this work. ...
Most of the available plan recognition techniques are based on the use of a plan library in order to infer user's intentions and/or strategies. Until some years ago, plan libraries were completely hand coded by human experts, which is an expensive, error prone and slow process. Besides, plan recognition systems with hand-coded plan libraries are not easily portable to new domains, and the creation of plan libraries require not only a domain expert, but also a knowledge representation expert. These are the main reasons why the problem of automatic generation of plan libraries for plan recognition, has gained much importance in recent years. Even when there is considerable work related to the plan recognition process itself, less work has been done on the generation of such plan libraries. In this paper, we present an algorithm for learning hierarchical plan libraries from action sequences, based on a few simple assumptions and with little given domain knowledge, and we provide a novel mechanism for supporting interleaved plans in the input example cases.
ResearchGate has not been able to resolve any references for this publication.