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Plan library evolution for shared steps  

Plan library evolution for shared steps  

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Article
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In the context of Computer Aided Process Planning (CAPP), feature recognition as well as the generation of manufacturing process plans are very diffi cult problems. The selection of the best manufacturing process plan usually involves not only measurable factors, but also idiosyncrasies, preferences and the know-how of both the company and the manu...

Citations

... design and process planning optimizations). For example, an agent for the CAPP activity has been developed in previous works FORRADELLAS, 2006a;MARCHETTA FORRADELLAS, 2006b;FORRADELLAS, 2007). This agent is capable of learning process planning patterns by observing decisions made by the manufacturing engineer. ...
... As far as we know, the automatic acquisition of hierarchical plan libraries, from unlabeled example cases, has not been achieved yet. In [16], some preliminary ideas and an algorithm for learning hierarchical plan libraries were pre- sented. ...
Article
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.
... In [16], some preliminary ideas and an algorithm for learning hierarchical plan libraries were presented. 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 [16], some ideas for acquiring hierarchical plan libraries from unlabeled examples were presented. ...
... Some of the works mentioned above automate some of the tasks that are needed in order to build plan libraries, and others propose methods for automatically generating plan libraries that are limited in expressiveness (notable exceptions are [16], and the early work presented in [4]). However, as far as we know, the automatic acquisition of hierarchical plan libraries, from unlabeled example cases, has not been achieved yet, and very little work has been done in supporting interleaved plans in action sequences used for learning. ...
Article
Full-text available
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.
Article
Full-text available
Modern organization paradigms within manufacturing enterprises have arose in last years, like Agile Manufacturing and collaboration, in order for enterprises to increase their productivity and be more competitive in front of shorter due dates and increasing product qualities required by customers. Most previous works on PLM and currently available systems are usually focused on the use of additional information to support business processes, and integrate limited information of lower-level applications (CAD, CAPP, etc). However, little emphasis has been put on making products more intelligent during their complete lifecycle, in order to exploit PLM information for improving their development and management. In this paper, a framework based on intelligent agents is proposed, for giving products active behaviors, in order to assist people involved in PLM to reduce lead times and costs, and improving product quality. Application of the proposed framework to a product definition example is presented as a case study.