Current research topics:

1. Uncertainty and hostility management in multi-agent systems:
   The goal of this research is to address competition within an organization, in which knowledge sharing is impossible. We develop and present an incomplete game theoretical based decision making method for competitive agents. (Received 2 best paper awards)

2. Agent-based Software Engineering (Agent-SE) Methodology:
   In this research we formalize the development process of multiagent systems as well as the knowledge representation and sharing of agents for cooperative and coordinative agents and use the results in large scale multi-agent system design. (2 keynote lectures)

3. Methodological support for interactive software agents:
   The goal of this research is to devise theories, techniques and measures for enhancing quality and reliability of agent-based systems. Unique points with this research are (a) focus on agent system reliability and quality; (b) focus on agent interactions by starting with a complete set of possible interactions among software agents; and (c) focus on decision making based on multiple threads of control rather than reasoning based on a single thread of control.

4. Distributed Software Agents for Network Fault Management:
   This is a major contribution in the sense that it offers a radically different local solution to network fault detection as opposed to the centralized tools that are commonly used in practice. (Received 2 best paper awards)

5. Distributed Multi-Agent Learning and Tutoring System based on Learning Ecology:
   The goal of the research is to develop an intelligent tutoring system (ITS) that adapts the delivery of instruction according to the learner's needs, by taking into account learner's motivation states. Due to computational convenience, many other systems rely only on the learner response to exercises to assess his/her needs. In our approach, however, we looked at one step deeper, the learner's learning drives, in order to find out what parameters affect the willingness to engage in learning.

6. Knowledge management in automatic software design:
   In Software Creation project we build a computer aided software engineering (CASE) tool that can imitate the design steps of human designers. We argue that there are two types of knowledge involved in human design: detailing knowledge represented by conversion and detailing rules; and the knowledge required for hierarchical expansion, represented by micro design rules. The former refers to the design product knowledge and the latter maps to the design process knowledge. My role in this project was to clarify the ideas and development of the first and supervising the later versions of the CASE tool. (Received 1 best paper award)

7. Methodologies for automated requirement acquisition and object-oriented programming:
   The goal of this research is to develop a methodology to automate natural language requirements analysis and class model generation based on the Rational Unified Process (RUP). Use-case language schemas are proposed to reduce the complexity and vagueness of natural language. Some rules are identified and used to automate the generation of the class model from use-case specifications. A CASE tool named UCDA is implemented to support the methodology. UCDA can assist the developer to generate use-case diagrams, use-case specifications, robustness diagrams, collaboration diagrams and class diagrams in Rational Rose. It helps accelerate requirements analysis and class modeling, and reduce the time to market in software development.

8. Intelligent project lifecycle knowledge management and decision support:
   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 research 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.

9. Commercial off-the-shelf software components evaluation method using agent technology:
   The target problem for this research is selected to bring together the agent-based and decision support methodologies. This provides us with a test bed for methods of interaction (e.g., negotiation, competition, etc.) and traditional decision theories (e.g., maximum utility, game theory, etc.). The target has a very high potential for commercialization.

10. Knowledge Orchestration using Web Agents:
    Over past decade, we have seen a growing interest in Knowledge Management (KM) solutions for knowledge assets in software industry including management of design blueprints, code, test, documents, as well as individual and group experiences (knowledge-bases, etc.), project specific documents (project bases, case bases, etc.), organizational policies, standards, etc. It is assumed that Knowledge Management (KM) solutions must: (1) Incorporate the management of knowledge assets with environments that facilitate and encourage interaction between individuals (people or departments). (2) Allow for dynamic classification and distribution of knowledge, and be able to adapt to changing contexts and personal styles. (3) Incorporate efficient (fast and effortless) retrieval mechanisms. Considering internal structure of organizations, people are demanding more autonomy, and more sense of ownership, shared power and participation. Consequently, organizations and their supporting systems must be able to allow for a degree of negotiation and adaptability in order to accommodate individual participation. With regard to these demands, multiagent systems (MAS) and potential social ability of MAS, seem well suited for knowledge management and organizational modeling.
    As opposed to experience factory and similar methods based on formal and large scale data collection, nowadays, software companies are practicing managing their knowledge assets using the WWW technology. New solutions try to push more intelligence on the documents and manage them (e.g., using semantically reach documents using semantic web, web services, RDF, and RDF schema, ontology vocabulary, etc.) However this vision brings other issues such as centralized KM solutions (currently implied by ontology-based methods). Such solutions usually require high operational costs and intensive contents administration. Using agent technology allows knowledge orchestration in a decentralized way with very little burden on administration.
    In this research, knowledge orchestration is equivalent to peer-to-peer knowledge management using models of organizational interactions characterized by three concepts: flow of information among individuals (people or departments), decentralized management and modularization. The agents work on a body of information and have reasoning and representation mechanisms appropriate for that type of information. Reusable modules (library, package, subsystems, COTS, etc.) for representing and reasoning with the body of information will further reduce the long term operational costs.

11. Intelligent Software Measurement System:
    Software measurement, in order to be effective, must be focused on specific goals; applied to all life-cycle products, processes and resources; and interpreted based on characterization and understanding of the organizational context, environment and goals. The Goal-Question-Metric (GQM) was developed in response to the need for a goal-oriented approach that would support the software measurement. A GQM model starts with a measurement goal. The goal is refined into several questions, and then each question is refined again into metrics.
    In this research the Intelligent Software Measurement System (ISMS) is developed following the goal-driven software measurement process. In the ISMS project we automate the 10 steps of the process. The main development tasks of ISMS are eliciting the knowledge and experience from software measurement experts, representing it in a flexible yet well-structured way, and building a knowledge base infrastructure for the system. Using the knowledge infrastructure, ISMS provides users with a series of interactive screens and views which guide them through the goal-driven process.

Old research topics:

1. Intelligent Agents for Electronic Commerce
2. Qualitative Functional Reasoning
3. Qualitative Sensitivity Analysis
4. Qualitative Reasoning in Supervisory Control