Learning to learn: From smart machines to intelligent machines

Since its birth, more than five decades ago, one of the biggest challenges of artificial intelligence remained the building of intelligent machines. Despite amazing advancements, we are still far from having machines that reach human intelligence level. The current paper tries to offer a possible explanation of this situation. For this purpose, we make a review of different learning strategies and context types that are involved in the learning process. We also present the results of a study on cognitive development applied to the problem of face recognition for social robotics.     

Valuing pilot projects in a learning by investing framework: An approximate dynamic programming approach

We introduce a general discrete time dynamic framework to value pilot project investments that reduce idiosyncratic uncertainty with respect to the final cost of a project. The model generalizes different settings introduced previously in the literature by incorporating both market and technical uncertainty and differentiating between the commercial phase and the pilot phase of a project. In our model, the pilot phase requires N$N$ stages of investment for completion. With this distinction we are able to frame the problem as a compound perpetual Bermudan option. We work in an incomplete markets setting where market uncertainty is spanned by tradable assets and technical uncertainty is idiosyncratic to the firm. The value of the option to invest as well as the optimal exercise policy are solved by an approximate dynamic programming algorithm that relies on the independence of the state variables increments. We prove the convergence of our algorithm and derive a theoretical bound on how the errors compound as the number of stages of the pilot phase is increased. We implement the algorithm for a simplified version of the model where revenues are fixed, providing an economic interpretation of the effects of the main parameters driving the model. In particular, we explore how the value of the investment opportunity and the optimal investment threshold are affected by changes in market volatility, technical volatility, the learning coefficient, the drift rate of costs and the time to completion of a pilot stage.     

The development of a formative scenario-based computer assisted assessment tool in psychology for teachers: The PePCAA project

Formative computer assisted assessment has become increasingly attractive in Higher Education where providing useful feedback to large numbers of students can be difficult. However, the nature of such assessments has often been limited to objective questions such as multiple-choice. This paper reports on the development and initial trialling of a more innovative, formative use of computer assisted assessment in a Higher Education context. The European funded PePCAA (Pedagogical Psychology Computer Assisted Assessment) project developed a series of scenario-based computer-delivered formative assessments of pedagogical psychology for teachers and trainee teachers, using a range of software features, including the addition of confidence measurement. The project had a two-fold aim: to provide a tool to improve understanding of pedagogical psychology and to explore the potential of more innovative techniques of computer assisted assessment to motivate students and to assess deeper learning. The combination of computer-delivered formative assessment with innovative question styles and confidence ratings is believed to be unique for pedagogical psychology. Scenarios were based on realistic classroom situations and focused on problem solving or on utilising best practice. The PePCAA Learning Assessment Circle (PLAC) provided a framework for indexing the kinds of processes required of users. In the UK, small scale trialling involved a total of 23 teacher trainees such that each assessment was attempted by about seven participants. Participants completed evaluation questionnaires after each assessment. Responses from learners indicated that the UK scenarios were generally very well received and had at least partly achieved the aim of stimulating deeper learning. Transfer of assessments between countries proved more difficult than expected. The next stage of development should be to conduct a larger pilot, thus allowing full investigation of the reliability and validity of the assessments. There is also scope for further development of the PePCAA approach and for its application in other subjects.     

New product development team intelligence: Antecedents and consequences

Our study investigated the effect of team knowledge on new product development (NPD). By investigating 207 NPD projects, we found that the declarative and procedural knowledge of the team and their use of IT had a positive influence on the team's knowledge base; and that the higher the functional diversity of the project team, the greater their overall knowledge. We also found that team knowledge positively impacted new product creativity and success in the market place.     

Developing a knowledge-based perspective on coordination: The case of global software projects

We have attempted to bring together two areas which are challenging for both IS research and practice: forms of coordination and management of knowledge in the context of global, virtual software development projects. We developed a more comprehensive, knowledge-based model of how coordination can be achieved, and\illustrated the heuristic and explanatory power of the model when applied to global software projects experiencing different degrees of success. We first reviewed the literature on coordination and determined what is known about coordination of knowledge in global software projects. From this we developed a new, distinctive knowledge-based model of coordination, which was then employed to analyze two case studies of global software projects, at SAP and Baan, to illustrate the utility of the model.     

An ontology-based support for product conceptual design

The research presented in this paper is a follow-up of our prior work involving the development of a graphical modeling tool to support designers at the conceptual design stage. To close the loop for supporting designers in generating design concepts flexibly, fast, and easily, an ontology-based approach for knowledge management that works along with the graphical modeling tool is discussed. Ontology and databases for the tool are developed to promote the systematic capture of design knowledge and efficient reuse of the design knowledge selection. In order to locate the proper information and query the data from the databases, the relationship between the ontology and databases, the data analysis process, ontology enrichment, and the ontology-based query engine are built to offer users multiple design results according to users’ requirements. A tire design example is presented to demonstrate the proposed approach. The ontology-based method described in this paper can help retrieve and save the complex relations, support the reasoning, integrate heterogeneous data resources and offer users more accurate, proper and comprehensive data.     

Prediction of vehicle reliability performance using artificial neural networks

Product development is an important but also dynamic, lengthy and risky phase in the life of a new product. The optimisation of the product development phase through extensive knowledge of the involved procedures is believed to reduce the risks and improve the final product quality. Artificial intelligence and expert systems have been used successfully in optimising the development phase of some new products as it will be demonstrated by the first sections of this publication. This paper presents the first module of an expert system, a neural network architecture that could predict the reliability performance of a vehicle at later stages of its life by using only information from a first inspection after the vehicle’s prototype production. The paper demonstrates how a tool like neural networks can be designed and optimised for use in reliability performance predictions. Also, this paper presents an optimisation methodology that enabled the neural network to deal with the limited amount of available training data, common during new product development, and to finally achieve acceptable prediction performance with small error. A case example is presented to demonstrate the methodology.     

A dynamic workflow framework for mass customization using web service and autonomous agent techniques

Custom software development and maintenance is one of the key expenses associated with developing automated systems for mass customization. This paper presents a method for reducing the risk associated with this expense by developing a flexible environment for determining and executing dynamic workflow paths. Strategies for developing an autonomous agent-based framework and for identifying and creating web services for specific process tasks are presented. The proposed methods are outlined in two different case studies to illustrate the approach for both a generic process with complex workflow paths and a more specific sequential engineering process.     

基于ArcObject和GM(1,1)的海岸线预测系统

海岸线变化对海岸带地区的可持续发展有重要的影响作用。该文以 Microsoft C#.NET为软件平台,应用ESRI公司提供的组建对象模型对象库——ArcObjects,利用GM(1,1)模型,通过设置预测原点和侧线,并与海岸线矢量数据进行叠加,得到若干GM(1,1)原始数列,由此计算出海岸线变化的预测点,实现了对海岸线变化的趋势预测,并将该预测系统应用于辽宁省国土资源规划地理信息系统中。