Category Archives: Machine Learning / Deep Learning

FASTrak-APT: Case and Constraint-Based Construction Project Planning System

Lee, K. J., Kim, H. W., Lee, J. K. Kim, T. H., “FASTrak-APT: Case and Constraint-Based Construction Project Planning System”, AI Magazine, vol.19, no.1, pp.13-24, Spring, 1998.pdf

Abstract

To effectively generate a fast and consistent apartment construction project network, Hyundai Engineering and Construction and Korea Advanced Institute of Science and Technology developed a case- and constraint-based project-planning expert system for an apartment domain. The system, FASTRAK- APT, is inspired by the use of previous cases by a human expert project planner for planning a new project and the modification of these cases by the project planner using his/her knowledge of domain constraints. This large-scale, case-based, and mixed-initiative planning system, integrated with intensive constraint-based adaptation, utilizes semantic-level metaconstraints and human decisions for compensating incomplete cases imbedding specific planning knowledge. The case- and constraint-based architecture inherently supports cross-checking cases with constraints during system development and maintenance. This system has drastically reduced the time and effort required for initial project planning, improved the quality and completeness of the generated plans, and is expected to give the company the competitive advantage in contract bids for new contracts.

A Peer-to-Peer CF-Recommendation for Ubiquitous Environment

Kim, H., Lee, K., Kim, J., A Peer-to-Peer CF-Recommendation for Ubiquitous Environment, Lecture Notes in Computer Science 4088: 678-683, 2006.pdf

Abstract

In ubiquitous environment where all entities can freely connect and collaborate with each other from anywhere, the amount of accessible information is overwhelming and desired information often remains unfound. So there is a growing need to provide the personalized recommendation services for the customers in ubiquitous space. This paper suggests a UREC_P2P (U-Recommendation by peer-to-peer), a recommendation procedure in ubiquitous environment adopting P2P technologies combined with collaborative filtering algorithm. UREC_P2P is implemented and comparatively evaluated with a CFbased recommender system in client-server environment. The evaluation result shows that UREC_P2P has a good potential to be a preeminent and realistic solution to the recommendation problems encountered in ubiquitous environment.