A knowledge based system for linking information to support decision making in construction

This work describes the development of a project model centred on the information and knowledge generated and used by managers. It describes a knowledge-based system designed for this purpose. A knowledge acquisition exercise was undertaken to determine the tasks of project managers and the information necessary for and used by these tasks. This information was organised into a knowledge base for use by an expert system. The form of the knowledge lent itself to organisation into a link network. The structure of the knowledge-based system, which was developed, is outlined and its use described. Conclusions are drawn as to the applicability of the model and the final system. The work undertaken shows that it is feasible to benefit from the field of artificial intelligence to develop a project manager assistant computer program that utilises the benefit of information and its link

Dynamic Poisson Factorization

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed preferences and behaviors of users are assumed to be generated without order. These assumptions limit the explorative and predictive capabilities of such models, since users' interests and item popularity may evolve over time. To address this, we propose dPF, a dynamic matrix factorization model based on the recent Poisson factorization model for recommendations. dPF models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions. We derive a scalable variational inference algorithm to infer the latent factors. Finally, we demonstrate dPF on 10 years of user click data from arXiv.org, one of the largest repository of scientific papers and a formidable source of information about the behavior of scientists. Empirically we show performance improvement over both static and, more recently proposed, dynamic recommendation models. We also provide a thorough exploration of the inferred posteriors over the latent variables.Comment: RecSys 201

Expert systems and finite element structural analysis - a review

Finite element analysis of many engineering systems is practised more as an art than as a science . It involves high level expertise (analytical as well as heuristic) regarding problem modelling (e .g. problem specification,13; choosing the appropriate type of elements etc .), optical mesh design for achieving the specified accuracy (e .g . initial mesh selection, adaptive mesh refinement), selection of the appropriate type of analysis and solution13; routines and, finally, diagnosis of the finite element solutions . Very often such expertise is highly dispersed and is not available at a single place with a single expert. The design of an expert system, such that the necessary expertise is available to a novice to perform the same job even in the absence of trained experts, becomes an attractive proposition. 13; In this paper, the areas of finite element structural analysis which require experience and decision-making capabilities are explored . A simple expert system, with a feasible knowledge base for problem modelling, optimal mesh design, type of analysis and solution routines, and diagnosis, is outlined. Several efforts in these directions, reported in the open literature, are also reviewed in this paper

Real time resource scheduling within a distributed collaborative design environment

Operational design co-ordination is provided by a Virtual Integration Platform (VIP) that is capable of scheduling and allocating design activities to organisationally and geographically distributed designers. To achieve this, the platform consists of a number of components that contribute to the engineering management and co-ordination of data, resources, activities, requirements and processes. The information required to schedule and allocate activities to designers is defined in terms of: the designers' capability to perform particular design activities; commitment in terms of the design activities that it is currently performing, and capacity to perform more than one design activity at the same time as well as the effect of increased capacity on capability. Previous approaches have been developed by the authors to automatically allocate resources to activities [1-3], however these approaches have generally been applied either within the context of real-time allocation of computational resources using automated design tools, or in the planning of human resources within future design projects and not for the real-time allocation of activities to a combination of human and computational resources. The procedure presented here is based upon this previous research and involves: the determination of the design activities that need to be undertaken on the basis of the goals that need to be achieved; identification of the resources that can undertake these design activities; and, the use of a genetic algorithm to optimally allocate the activities to the resources. Since the focus of the procedure is toward the real-time allocation of design activities to designers, additional human issues with respect to scheduling are considered. These human issues aspects include: consideration of the improvement in performance as a result of the experience gained from undertaking the activity; provision of a training period to allow inexperienced designers the opportunity to improve their performance without their performance being assessed; and the course of action to take when a designer is either unwilling or unable to perform an activity

The mimR Package for Graphical Modelling in R

The mimR package for graphical modelling in R is introduced. We present some facilities of mimR, namely those relating specifying models, editing models, fitting models and doing model search. We also discuss the entities needed for flexible graphical modelling in terms of an ob ject structure. An example about a latent variable model is presented.