Behrouz Far

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Noel B. Salazar

KU Leuven

David Seamon

Kansas State University

Shehzad Ashraf

International Islamic University Islamabad

Elana Fertig

Johns Hopkins University

Gordon N Fleming

Pennsylvania State University

Benny Nyambo

University of Zimbabwe

Marica Amadeo

Mediterranean University of Reggio Calabria

Jaydip Sen

Praxis Business School

Mohammed Najm Abdullah Al Salam

University of Technology/Iraq

Moriba Jah

The University of Texas at Austin

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Papers by Behrouz Far

Case studies on the application of the CORE model for requirements engineering process assessment [software engineering]

Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513)

Existing Requirements Engineering (RE) process assessment models lack the components necessary to... more Existing Requirements Engineering (RE) process assessment models lack the components necessary to provide enough information about the quality of a RE process. The concept of &amp;quot;COncern of Requirement Engineering&amp;quot; (CORE) and the assessment models proposed in our previous research provide a method and new perspectives to assess the quality of a RE process. The case studies presented in this paper provide a comprehensive view of the application of the CORE model. The advantages of using the model for RE process assessment are two fold: first, it is more flexible because the major COREs assess the main contents of the activities of a RE process. Second, the categories classified in the model allow RE process assessment based on several categories. This allows for process improvement in an incremental manner. The CORE model is part of our RE process development framework and is used to assess the quality of the RE process under development.

Modeling and implementation of software agents decision making

Proceedings of the Third IEEE International Conference on Cognitive Informatics, 2004., 2004

Software agents are knowledgeable, autonomous, situated and interactive software entities. Agents... more Software agents are knowledgeable, autonomous, situated and interactive software entities. Agents&#x27; interactions are of special importance when a group of agents interact with each other to solve a problem that is beyond the capability and knowledge of each individual. Efficiency, performance and overall quality of the multiagent applications depend mainly on how the agents interact with each other effectively. We suggest an agent model by which we can clearly distinguish different agent&#x27;s interaction scenarios. The model has five attributes: goal, control, interface, identity and knowledge base. Using the model, we analyze and describe possible scenarios; devise the appropriate reasoning and decision making techniques for each scenario; and build a library of reasoning and decision making modules that can be used readily in the design and implementation of multiagent systems.

Toward leveraging big value from data: chronic lymphocytic leukemia cell classification

Network Modeling Analysis in Health Informatics and Bioinformatics, 2017

The goal of Big Data analysis is delineating hidden patterns from data and leverage them into str... more The goal of Big Data analysis is delineating hidden patterns from data and leverage them into strategies and plans to support informed decision making in a diversity of situations. Big Data are characterized by large volume, high velocity, wide variety, and high value, which may represent difficulties in storage and processing. Research on Big Data repositories has contributed promising results that primarily address how to efficiently mine a variety of large volume of structured and unstructured data. However, innovative insights can emerge while leveraging the value characteristic of Big Data. In other words, any given data can be big if analytics can draw a big value from it. In this paper, we demonstrate the potential of five machine learning algorithms to leverage the value of medium size microscopic blood smear images to classify patients with chronic lymphocytic leukemia (CLL). The maximum majority voting method is used to fuse the predications made by the five classifier models. To validate this work, 11 CLL patients are refereed by flow cytometry equipment and the results are compared to the proposed classifier model. The proposed method proceeds through a sequence of steps while working with the lymphocyte images: it segments the lymphocyte images, extracts/selects features, classifies the selected features using five classifiers, and calculates the majority class for the test image. The proposed composite classifier model has an accuracy of 87.0%, true-positive rate of 84.95%, and 10.96% false-positive rate and can correctly identify 9 out of 11 patients as positive for CLL.

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A Qualitative Reasoning Model for Tradeoff Analysis in Multiple Objective Decision Making

Journal of Advanced Computational Intelligence and Intelligent Informatics, 2007

In Multi-Criteria Decision Making problems such as choosing a development policy, selecting softw... more In Multi-Criteria Decision Making problems such as choosing a development policy, selecting software products, or searching for commodities to purchase, it is often necessary to evaluate solution options in respect of multiple objectives. The solution alternative that performs best in all the objectives is the dominant solution, and it should be selected to solve the problem. However, usually the selection objectives are incomparable and conflicting, making it impossible to have a dominant solution among the alternatives. In such cases, tradeoff analysis is required to identify the objectives that can be optimized, and those that can be comprised in order to choose a winning solution. In this paper we present a tradeoff analysis model based on the principles of qualitative reasoning that provides visualization support for understanding interaction and tradeoff dependences among solutions evaluation criteria which affect the tradeoff among selection objectives. Moreover, the decision...

CBR in Design: Towards more creative system using adaptation knowledge

Design is a task that there can be no specific and completely predefined knowledge for that. CBR ... more Design is a task that there can be no specific and completely predefined knowledge for that. CBR is a kind of reasoning which is quite suitable for this kind of tasks. Since design without creativity is not that much valuable, taking this issue into account is gaining more popularity. In this paper, we want to investigate the role of adaptation

Intelligent Agents for Electronic Commerce

In this project we have proposed an agent model for EC components that blends the traditional exp... more In this project we have proposed an agent model for EC components that blends the traditional expert systems' reasoning engine with a multi-layer knowledge base, communication and documentation engines. EC is viewed as a society of software agents, such as customer, search, catalog, retailer, vendor, delivery and banking agents, that interact and negotiate with each other. Each agent has a

Software Creation: Towards Automatic Software Design By Simulating Human Designers

Software Engineering and Knowledge Engineering, 1993

This paper describes the outline, the approach and current status of the Software Creation projec... more This paper describes the outline, the approach and current status of the Software Creation project. The main idea is to follow the design steps of hu- man designers by extracting and reusing the de- sign process knowledge in a hierarchically orga- nized work process. The presently implemented system consists of a SDL CASE tool and an expert system for applying

Qualitative fault diagnosis in systems with nonintermittent concurrent faults: a subjective approach

IEEE Transactions on Systems, Man, and Cybernetics, 1993

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Improving Example Selection for Agents Teaching Ontology Concepts

Lecture Notes in Computer Science, 2006

We present a method to improve the positive examples selec- tion by teaching agents in a multi-ag... more We present a method to improve the positive examples selec- tion by teaching agents in a multi-agent system in which a team of agent peers teach concepts to a learning agent. The basic idea in this method is to let a teacher agent expand the features it uses to describe a concept in its ontology by additional features. This resembles the typical behav- ior of human teachers who describe concepts from difierent viewpoints in the hope that one of these viewpoints comes close to the viewpoint of a learner. The extended feature set is then used to select positive ex- amples that together with negative examples are communicated to the learner agent. The learner uses concept learning techniques to integrate the new concept into its own ontology. An experimental evaluation shows a signiflcant learning improvement compared to the previous approach.

Human knowledge to be applied to communications services

ICCT'98. 1998 International Conference on Communication Technology. Proceedings (IEEE Cat. No.98EX243)

This paper reports on the use of secretary&#39;s knowledge for telephone handling. The secret... more This paper reports on the use of secretary&#39;s knowledge for telephone handling. The secretary&#39;s knowledge has been systematically acquired by `acquisition as modeling&#39;, and they may be reconstructed by an electronic secretary in a personal computer, again systematically. Knowledge models reported are, those for conversation, information collection, and various decisions. The application of the knowledge models to communications are discussed

On the Usage of Network Visualization for Multiagent System Verification

Lecture Notes in Social Networks, 2014

Multiagent Systems (MAS) consists of many software agents that interact to each other to perform ... more Multiagent Systems (MAS) consists of many software agents that interact to each other to perform their actions and achieve system goals. Due to the growing demand of Distributed Software Systems (DSS) and MAS as a branch of DSS, the verification of these systems has taken a special attention. The verification of these systems is required because MAS and DSS are large scale systems, where a failure can result in a huge amount of cost or damage. One major field is verification of MAS designs to prevent cost of fixing problems after implementation and deployment. The agents and interactions among them form a network of agents. This network can be used for verification of MAS from different perspectives and by various techniques. Visualizing agents’ networks can lead to detect a special type of unexpected behavior in MAS referred to as emergent behaviors and implied scenarios. These unexpected behaviors are more probable in large scale systems because the functionality and control are distributed and there is lack of central controller in MAS and DSS. Consequently, a new scenario can be implied to the system at run time which may not be an acceptable behavior in the system. In this article, visualization techniques are applied to form three different networks extracted from the designs of MAS for this purpose. These networks are used to detect emergent behaviors and implied scenarios in the system. The methodology, system architecture, data preparation and visualization, required network definitions, and results are verified through case studies.

Uncertainty management in software engineering: Past, present, and future

2009 Canadian Conference on Electrical and Computer Engineering, 2009

Software development has significantly matured in the last decade. However, one of the critical c... more Software development has significantly matured in the last decade. However, one of the critical challenges today is uncertainty inherent to every aspect of software development including requirement specifications, design, coding, and testing. In this paper, we propose a framework for uncertainty management in software engineering. The framework is used to model uncertainty inherent to software development activities and manage their consequences. The framework consists of four main phases: identification and prioritization, modeling and analysis, management and planning, and monitoring and evaluation. Commercial off-the-shelf (COTS)-based development is selected as an example to illustrate how the proposed framework is used in a simple but intuitive case study to represent uncertainty and manage its consequences.

Mining Adaptation Rules from Cases in CBR Systems

Discovery Science, 1999

Case based reasoning (CBR) is a well known framework to cope with ill-structured tasks, where no ... more Case based reasoning (CBR) is a well known framework to cope with ill-structured tasks, where no enough domain knowledge is available [2]. The main objective of CBR is to address the knowledge acquisition bottleneck. Namely, in CBR the reasoner does not make effort to build an abstract model for domain knowledge to solve the problem, instead, during the problem solving, it relies on the past similar cases, and attempts to find the appropriate solution for the problem at hand, by modifying the past similar solutions. However, CBR systems also require substantial knowledge acquisition effort (e.g. acquiring cases, case vocabulary, retrieval knowledge, adaptation knowledge [5]). This knowledge traditionally is derived from a domain expert. Accordingly, although the expert can not propose an abstract model to support the domain, s/he attempts to define some regularities in the domain, that makes it possible to reason with the cases. In fact, the knowledge necessary to solve the problem has been depicted in the cases. The expert defines the skill of using the right case in the right place (similarity), and also defines a formalism to modify the old cases such that address the new problem (adaptation). Defining this knowledge, if precision matters needs an accurate model of domain, proposed by expert (which is normally not available in CBR domains), and if precision is not assumed as a requirement, may fail to demonstrate acceptable reasoning skill. However, since in CBR there is no well-defined background knowledge, defining this knowledge by expert, constrains the system with a static incomplete reasoning skill, that does not evolve with new experiences, even though this skill can be acquired through the new cases. Furthermore, adaptation is a compensatory part of CBR [1]. Adaptation process can compensate the shortcomings in other steps of reasoning process, by appropriately modifying a weak match to obtain a reasonable solution. Accordingly automating the process of acquiring adaptation knowledge, in this work, has been proposed in the means of increasing the reasoning skill.

Negotiation coalitions in group-choice multi-agent systems

Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems - AAMAS '06, 2006

In Group-Choice Decision Making (GCDM) where a number of stakeholders are involved in choosing a ... more In Group-Choice Decision Making (GCDM) where a number of stakeholders are involved in choosing a single solution from a set of available solution options, it is common for the stakeholders to form coalitions during negotiations in order to increase their individual welfare. It is also common to use Multi-Agent Systems (MAS) to automate GCDM processes. In such MAS, agents have to form coalitions like their human counterparts. Within each coalition, the individual agents behave according to the strategies of their clients. In this paper we present a coalition formation model which can employ multiple coalition formation algorithms. The model is motivated by a real-world group-choice multi-agent system that we developed; in the system, agents can form coalitions dynamically based on the similarity between their preferences, and based on the ownership criteria. Finally, this paper presents simulation results that illustrate the operational effectiveness of our coalition formation model.

Qualitative Reasoning Model for Tradeoff Analysis

Lecture Notes in Computer Science, 2005

An Agent Negotiation Engine for Collaborative Decision Making

Lecture Notes in Computer Science, 2006

Negotiation engines are major components of autonomous agents, because negotiation is one of the ... more Negotiation engines are major components of autonomous agents, because negotiation is one of the most important types of agent interaction. Thus far, most negotiation engines rely on analytic techniques to maximize the social welfare of agent communities. Such engines are developed with total disregard of the possibility of enabling agents to analyze offers made by their negotiation opponents. The analysis of the offers leads to making tradeoffs that result into agreeing on (selecting) solution options that maximize the social welfare of the negotiation agents. Therefore, this paper presents an agent negotiation engine that supports the following: evaluation of solution options, analysis of tradeoffs, analysis of offers, and management of negotiation deadlocks. Moreover, the paper presents a simulation experiment that illustrates the capabilities of the negotiation engine.

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Using prediction to provide decision support for the elicitation of user preferences

Canadian Conference on Electrical and Computer Engineering, 2005., 2005

In multi-criteria decision making problems such as selecting development policies, selecting soft... more In multi-criteria decision making problems such as selecting development policies, selecting software products, or searching for commodities to purchase, it is necessary to have a precise model of the user preferences. Studies have revealed that often people are unable to state their preferences up front, and that they start to evaluate solution alternatives with a small set of high-value preferences; but change the value of those preferences as they discovery other solution features which they can incorporate into their preference models (B. Faltings et al., 2004). While, a variety of preference elicitation models have been proposed, limited or no effort has been made to utilize historical data to provide decision support for the elicitation of user preferences. In this paper, we discuss using neural net to take advantage of historical data, and provide decision support for developing user preference models, as well as preference value functions; from a set of high-value preferences. Moreover, we report results of using our technique to elicit the user preferences for evaluating and selecting a commercial-off-the-shelf software component

Towards providing decision support for COTS selection

Canadian Conference on Electrical and Computer Engineering, 2005.

The evolution of software engineering has led to component-based software development, which in t... more The evolution of software engineering has led to component-based software development, which in turn has engendered 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, development time and effort constraints, and expanding system requirements. However, the process of selecting COTS products is characterized by a multiplicity of challenges, which should be addressed in order to harness the benefits of COTS-based software development. In this paper we preset a model that splits the COTS selection process into layers; basing on the (intra-layer) activities which affect the choice of a decision support to address a particular challenge. Moreover, we evaluate the COTS selection methods in the reviewed literature according to how they address the challenges. Finally we present the functionalities of an ideal decision support system (DSS) for COTS selection, as well as the techniques for achieving the functionalities

Peripheral blood smear image analysis: A comprehensive review

Journal of pathology informatics, 2014

Peripheral blood smear image examination is a part of the routine work of every laboratory. The m... more Peripheral blood smear image examination is a part of the routine work of every laboratory. The manual examination of these images is tedious, time-consuming and suffers from interobserver variation. This has motivated researchers to develop different algorithms and methods to automate peripheral blood smear image analysis. Image analysis itself consists of a sequence of steps consisting of image segmentation, features extraction and selection and pattern classification. The image segmentation step addresses the problem of extraction of the object or region of interest from the complicated peripheral blood smear image. Support vector machine (SVM) and artificial neural networks (ANNs) are two common approaches to image segmentation. Features extraction and selection aims to derive descriptive characteristics of the extracted object, which are similar within the same object class and different between different objects. This will facilitate the last step of the image analysis process...

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Detecting Emergent Behavior in a Social Network of Agents

Lecture Notes in Social Networks, 2012

An effective and efficient approach in designing software systems to describe system requirements... more An effective and efficient approach in designing software systems to describe system requirements is using scenarios. A scenario, commonly shown as a message sequence chart or a sequence diagram, is a temporal sequence of messages sent between system components. Scenarios are appealing because of their expressive power and simplicity. Moreover due to the clear and concise syntactic of scenarios, they can be used to analyze the system requirements for general validity, lack of deadlock, and existence of emergent behavior. Emergent behavior or implied scenarios are specifications of behavior that are derived from compiling of all requirements together but are not explicitly specified in the set of scenarios. Although emergent behavior is not necessarily unwanted, nevertheless it is useful for system designers and engineers to be aware of its existence. Defining requirements using scenarios and conducting consequent analysis has been done for distributed systems as well as multi-agent system. In this research the requirements of a social network are described using scenarios. The scenarios are then used to detect emergent behavior using a systematic methodology. This is illustrated using a prototype of a social network of MAS for semantic search that blends the search and ontological concept learning.

Case studies on the application of the CORE model for requirements engineering process assessment [software engineering]

Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513)

Modeling and implementation of software agents decision making

Proceedings of the Third IEEE International Conference on Cognitive Informatics, 2004., 2004

Toward leveraging big value from data: chronic lymphocytic leukemia cell classification

Network Modeling Analysis in Health Informatics and Bioinformatics, 2017

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A Qualitative Reasoning Model for Tradeoff Analysis in Multiple Objective Decision Making

Journal of Advanced Computational Intelligence and Intelligent Informatics, 2007

CBR in Design: Towards more creative system using adaptation knowledge

Intelligent Agents for Electronic Commerce

Software Creation: Towards Automatic Software Design By Simulating Human Designers

Software Engineering and Knowledge Engineering, 1993

Qualitative fault diagnosis in systems with nonintermittent concurrent faults: a subjective approach

IEEE Transactions on Systems, Man, and Cybernetics, 1993

Download

Improving Example Selection for Agents Teaching Ontology Concepts

Lecture Notes in Computer Science, 2006

Human knowledge to be applied to communications services

ICCT'98. 1998 International Conference on Communication Technology. Proceedings (IEEE Cat. No.98EX243)

On the Usage of Network Visualization for Multiagent System Verification

Lecture Notes in Social Networks, 2014

Uncertainty management in software engineering: Past, present, and future

2009 Canadian Conference on Electrical and Computer Engineering, 2009

Mining Adaptation Rules from Cases in CBR Systems

Discovery Science, 1999

Negotiation coalitions in group-choice multi-agent systems

Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems - AAMAS '06, 2006

Qualitative Reasoning Model for Tradeoff Analysis

Lecture Notes in Computer Science, 2005

An Agent Negotiation Engine for Collaborative Decision Making

Lecture Notes in Computer Science, 2006

Download

Using prediction to provide decision support for the elicitation of user preferences

Canadian Conference on Electrical and Computer Engineering, 2005., 2005

Towards providing decision support for COTS selection

Canadian Conference on Electrical and Computer Engineering, 2005.

Peripheral blood smear image analysis: A comprehensive review

Journal of pathology informatics, 2014

Download

Detecting Emergent Behavior in a Social Network of Agents

Lecture Notes in Social Networks, 2012

Behrouz Far

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