Learning Function of Devices Using Qualitative Function Formation Technique

The goal of this research is developing methods for categorizing and learning function of objects from a formal description of their structure and behavior using Functional Reasoning (FR). A collective view on FR theories and techniques is presented, common assumptions and basic problems are identified. Qualitative Function Formation (QFF) technique is introduced. Some novel points are extending the common qualitative models to include interactions and timing of events, by defining coordinative relations, temporal and dependency constraints, and binding it with the conventional qualitative simulation. A function concept is defined as an interpretation of a persistence or an order in the sequence of qualitative states. Examples of application of QFF in categorization and learning function of objects are given

An Intelligent Project Lifecycle Data Mart-based Decision Support System

Mission critical decision making in enterprises depends heavily on intelligent systems for extracting, analyzing and interpreting information from multiple heterogeneous, distributed data and knowledge sources. It is assumed that data warehouses (DW), data marts (DM) are required for optimized data accessibility and use. This paper discusses issues with the current DW/DM systems and propose a novel architecture based on multi-agents technology to support information and knowledge extraction over distributed data sources in order to use them in the decision making process. The proposed framework is applied to a real-world project lifecycle case that is EPC (Engineering Procurement and Construction) project

Agent-Based Commercial Off-The-Shelf Software Components Evaluation Method

In the last decade, the world of software development has evolved rapidly. This evolution has led to Component-Based Software Development (CBSD), which in turn has generated tremendous interest in the development of plug-and-play reusable software, leading to the concept of Commercial Off The Shelf (COTS) software components. The use of COTS is increasingly becoming commonplace. This is mainly due to shrinking budgets, accelerating rates of COTS enhancement, reducing development time and effort constraints, and expanding system requirements. However, the COTS marketplace is characterized by a vast array of products and product claims, extreme quality and capability differences between products, and many products incompatibilities, even when they purport to adhere to the same standards. Therefore, there is need for a robust COTS evaluation methodology to help software developers select appropriate components for projects. A variety of COTS evaluation methods have already been proposed. These methods are based on either consensual opinion aggregation approach or regression models. However, both approaches are inadequate for the COTS Evaluation process. In this paper, we propose an agent-based COTS evaluation method, which models each of the players as either a cooperative or a competing agent that is capable of making its own decisions to meets its goals. In this model, there is an administrator agent that collects, evaluates, and combines knowledge from different areas of expertise (Roles) to offer support in the COTS selection process. This way, we circumvent COTS evaluation problems associated with the consensual opinion aggregation and the regression models approaches. 1