Employee experience is a valuable asset for any company. A system which can store, retrieve and adapt these experiences to meet the requirements of new scenarios can play an important role in corporate knowledge management. Case-Based Reasoning provides an excellent mechanism for this because it allows the capture and reuse of past experiences, which in this paper involves the generation of expert-level support in response to questions raised by British Telecommunications eld engineers. However, experience capture is hard in dynamic environments involving multi-modal communication and content. This paper examines the context of this research and details the work which has been completed thus far, as well as potential next steps.
This research project is a collaboration between British Telecommunications (BT) and Robert Gordon University to produce a system which uses experiential content gathered from users as a case-base to answer new queries. The query and answer process will take place while engineers are out in the eld, giving them access to the support they require within the dynamic environment of their jobs. This would facilitate the exchange of knowledge and experience between employees within the company and assist in the development of a `corporate memory', which stores the relevant experiences of every engineer in the company, preventing these assets from being lost if an employee were to leave BT.
This paper is structured as follows: section 2 gives an overview of the context of the project and section 3 describes its contributions. Section 4 discusses related work and research. The report concludes with a description of the research which has been completed so far and details future work and next steps the research could take.
Copyright © 2017 for this paper by its authors. Copying permitted for private and academic purpose. In Proceedings of the ICCBR 2017 Workshops. Trondheim, Norway
The goals of this project are to develop a means of capturing experiential knowledge from BT engineers and to produce an application which can learn to provide expert-level support in the eld using that information. The system should be able to support engineers by retrieving relevant results and adapting to new situations accordingly, producing results which are applicable in the real-world.
In the eld, an engineer is only able to access notes (historical task records) which are part of the current task. These notes are plain text and contain customer and location information, but rarely indicate what piece of equipment the fault may relate to (as this may not be known at the time of task allocation), the context of the task, or similar tasks. An engineer is expected to rely heavily on past experiences and training in order to diagnose and solve problems, but this can fail if the task has novel, unusual or specialist elements. Some of these failures may be avoided if there existed a means of drawing upon the experiences of engineers who had previously encountered similar problems.
To give a real example, a BT power engineer was called out to a `low-voltage' alarm in a rural exchange. It transpired that the mains power had failed and the back-up generator had not started, so the exchange was beginning to lose power and risked the network going down for nearby customers. The engineer was unsure of what the exact fault with the back-up generator could be and spent some time attempting to diagnose the problem before calling for help. It was only when an engineer with more experience arrived that the fault was eventually diagnosed - the ac/mc contactor within the generator needed replaced. This was a time-dependent and critical fault which could have had important business repercussions and was only solved because a more experienced engineer was able to attend on short notice. If experience and knowledge could be more e ectively transferred between engineers, then we could severly reduce the time taken to solve these faults, as well as the manpower required to do so and the risk of failing to complete time-critical faults.
We aim to use the notes and other knowledge assets (including video, photo and audio content) as the case base for an intelligent system which can reply to engineer's queries and propose a solution. This would alleviate pressure upon engineers by giving them access to a `digital expert' which could draw upon historical experiences from the entire work force to provide support in the eld. 3
As this project is a collaboration between a representative of industry and a university, it is important that the research provides an academic contribution, but remains viable for use in a commercial setting. There often exists a disconnect between the two, fuelled by the exploratory nature of research and the fact that 'state-of-the-art' measures may involve expensive procedures or equipment which do not have commercial viability. Therefore, this project is a case study of a research project which has both academic importance and good business viability and o ers contributions in both areas.
The main contribution of this project towards industry will be to improve information access for engineers in the eld and facilitate the exchange of knowledge and expertise by using an intelligent system. By doing so, this system should ultimately improve an engineer's ablity to complete a task and, at an organisational level, increase overall engineer productivity.
The contribution of this project to academia is to develop a dynamic decision support system which can operate on a large scale across multiple engineer domains. This project will ultimately showcase a system which can retrieve relevant results from vast quantities of complex, inter-related multi-media data. 4
Due to its very nature of reusing and adapting previous examples, Case-Based
Reasoning (CBR) is the branch of machine learning which most closely re ects
the goals of the project. Using CBR techniques to facilitate knowledge ow
between users is not a new concept, having been pursued with di ering levels of
success for a number of years. Of these, many projects have speci cally targeted
domain experts within a pre-identi ed industry niche, including food quality
control [
In [
A Case Retrieval Net (CRN) is a CBR framework which facilitates the return
of a small number of cases in a large case base. CRNs use a memory structure
that stores both the contents of the case base and similarity knowledge between
cases [
A Siamese Neural Network (SNN) architecture consists of two neural
networks that share identical weights and are joined at one or more layers.
Introduced in [
Recent research has demonstrated that SNNs are able to generate
object-toobject similarities after being trained with relatively few examples or in datasets
where a vast number of classes exist [
Much of our recent work has been gathering data to determine the industrial and academic context of this project. In particular, we reviewed literature featuring industry examples of experience capture and knowledge transfer systems to see how others have dealt with similar problems. In addition, we gathered data from within BT to establish the speci c business context of the project. This involved determining the available information sources for use by engineers, how they are used on specifc tasks and in what areas they are lacking.
One of the key aspects of this project is the development of a large and dynamic case base which can be used and updated in real-time throughout the day. Often, retrieval from a huge case base can be extremely costly. We are examining methods of reducing this cost without sacri cing case base coverage or retrieval accuracy through similarity-based retrieval in a CRN, but learning similarity knowledge in a huge system can be expensive. To this end we are performing experiments to learn similarity between cases in a quick and inexpensive manner. Recently we have examined generating case-to-case similarity knowledge by using an SNN and have demonstrated that this is capable of developing case-to-case similarity knowledge suitable for similarity-based retrieval.
In future work, we would like to experiment with training SNN on limited data to ascertain whether they can successfully learn similarity knowledge. Also, we would like to examine populating the IEs of a CRN using the values developed by the output of an SNN to see whether we can return improved results with spreading activation.