K-means Clusteringfor Software faults prediction

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Meenu Singla ◽  
Bhavtosh Mishra
Keyword(s):  
Development Process ◽  
Case Based Reasoning ◽  
Multi Agent System ◽  
Software Faults ◽  
Rule Based ◽  
Software Failures ◽  
Knowledge Based ◽  
Clustering And Classification ◽  
Multi Agent

The occurrence of defects in the software system has become prominent problem in development process of software. A software fault refers to wrong transition within software that causes the product to act in an unintended manner. Faults are the root causes of software failures. There are various types of software faults such as coding, design and requirement faults, data problems . clustering and classification of faults are important issues.ICM are used for these like Expert System (ES), Multi Agent System (MAS), Knowledge Based Reasoning (KBR), Genetic Algorithm (GA), Case Based Reasoning (CBR), Data Mining (DM)techniques, Rule Based Reasoning (RBR), and Artificial Neural Networks (ANN) are the components of ICM CBR and RBR are the parts of KBR.

Case-Based Reasoning and Some Typical Applications

2014 ◽  
pp. 229-267 ◽  
Author(s):  
Durga Prasad Roy ◽  
Baisakhi Chakraborty
Keyword(s):  
Problem Solving ◽  
Machine Intelligence ◽  
Case Based Reasoning ◽  
Engineering System ◽  
Yale University ◽  
Control Pattern ◽  
Clustering And Classification ◽  
How People Learn ◽  
Case Based

Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977–1993. CBR may be defined as a model of reasoning that incorporates problem solving, understanding, and learning, and integrates all of them with memory processes. It focuses on the human problem solving approach such as how people learn new skills and generates solutions about new situations based on their past experience. Similar mechanisms to humans who intelligently adapt their experience for learning, CBR replicates the processes by considering experiences as a set of old cases and problems to be solved as new cases. To arrive at the conclusions, it uses four types of processes, which are retrieve, reuse, revise, and retain. These processes involve some basic tasks such as clustering and classification of cases, case selection and generation, case indexing and learning, measuring case similarity, case retrieval and inference, reasoning, rule adaptation, and mining to generate the solutions. This chapter provides the basic idea of case-based reasoning and a few typical applications. The chapter, which is unique in character, will be useful to researchers in computer science, electrical engineering, system science, and information technology. Researchers and practitioners in industry and R&D laboratories working in such fields as system design, control, pattern recognition, data mining, vision, and machine intelligence will benefit.

Planning Agent for Geriatric Residences

2011 ◽  
pp. 1316-1322 ◽  
Author(s):  
Javier Bajo ◽  
Dante I. Tapia ◽  
Sara Rodríguez ◽  
Juan M. Corchado
Keyword(s):  
Health Care ◽  
Radio Frequency Identification ◽  
Adaptive Systems ◽  
Autonomous Agent ◽  
Case Based Reasoning ◽  
Multi Agent System ◽  
Agent System ◽  
Learning And Adaptation ◽  
Multi Agent ◽  
Case Based

Agents and Multi-Agent Systems (MAS) have become increasingly relevant for developing distributed and dynamic intelligent environments. The ability of software agents to act somewhat autonomously links them with living animals and humans, so they seem appropriate for discussion under nature-inspired computing (Marrow, 2000). This paper presents AGALZ (Autonomous aGent for monitoring ALZheimer patients), and explains how this deliberative planning agent has been designed and implemented. A case study is then presented, with AGALZ working with complementary agents into a prototype environment-aware multi-agent system (ALZ-MAS: ALZheimer Multi-Agent System) (Bajo, Tapia, De Luis, Rodríguez & Corchado, 2007). The elderly health care problem is studied, and the possibilities of Radio Frequency Identification (RFID) (Sokymat, 2006) as a technology for constructing an intelligent environment and ascertaining patient location to generate plans and maximize safety are examined. This paper focuses in the development of natureinspired deliberative agents using a Case-Based Reasoning (CBR) (Aamodt & Plaza, 1994) architecture, as a way to implement sensitive and adaptive systems to improve assistance and health care support for elderly and people with disabilities, in particular with Alzheimer. Agents in this context must be able to respond to events, take the initiative according to their goals, communicate with other agents, interact with users, and make use of past experiences to find the best plans to achieve goals, so we propose the development of an autonomous deliberative agent that incorporates a Case-Based Planning (CBP) mechanism, derivative from Case-Based Reasoning (CBR) (Bajo, Corchado & Castillo, 2006), specially designed for planning construction. CBP-BDI facilitates learning and adaptation, and therefore a greater degree of autonomy than that found in pure BDI (Believe, Desire, Intention) architecture (Bratman, 1987). BDI agents can be implemented by using different tools, such as Jadex (Pokahr, Braubach & Lamersdorf, 2003), dealing with the concepts of beliefs, goals and plans, as java objects that can be created and handled within the agent at execution time.

scholarly journals Artificial Intelligence-Virtual Trainer: An educative system based on artificial intelligence and designed to produce varied and consistent training lessons

2016 ◽  
Vol 231 (2) ◽  
pp. 110-124 ◽  
Author(s):  
Julien Henriet
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Ordered Set ◽  
Case Based Reasoning ◽  
Adaptation Process ◽  
Multi Agent System ◽  
Multi Agent ◽  
Different Parts ◽  
Case Based ◽  
Underlying Theme

AI-Virtual Trainer is an educative system using Artificial Intelligence to propose varied lessons to trainers. The agents of this multi-agent system apply case-based reasoning to build solutions by analogy. However, as required by the field, Artificial Intelligence-Virtual Trainer never proposes the same lesson twice, whereas the same objective may be set many times consecutively. The adaptation process of Artificial Intelligence-Virtual Trainer delivers an ordered set of exercises adapted to the objectives and sub-objectives chosen by trainers. This process has been enriched by including the notion of distance between exercises: the proposed tasks are not only appropriate but are hierarchically ordered. With this new version of the system, students are guided towards their objectives via an underlying theme. Finally, the agents responsible for the different parts of lessons collaborate with each other according to a dedicated protocol and decision-making policy since no exercise must appear more than once in the same lesson. The results prove that Artificial Intelligence-Virtual Trainer, however perfectible, meets the requirements of this field.

VERIFICATION OF THE MOBILE AGENT NETWORK SIMULATOR — A TOOL FOR SIMULATING MULTI-AGENT SYSTEMS

2008 ◽  
Vol 18 (05) ◽  
pp. 651-682 ◽  
Author(s):  
MARIO KUSEK ◽  
KRESIMIR JURASOVIC ◽  
GORDAN JEZIC
Keyword(s):  
Mobile Agent ◽  
Formal Model ◽  
Task Complexity ◽  
Network Simulator ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Network Bandwidth ◽  
Knowledge Based ◽  
Multi Agent

This paper deals with the verification of a multi-agent system simulator. Agents in the simulator are based on the Mobile Agent Network (MAN) formal model. It describes a shared plan representing a process which allows team formation according to task complexity and the characteristics of the distributed environment where these tasks should be performed. In order to verify the simulation results, we compared them with performance characteristics of a real multi-agent system, called the Multi-Agent Remote Maintenance Shell (MA–RMS). MA–RMS is organized as a team-oriented knowledge based system responsible for distributed software management. The results are compared and analyzed for various testing scenarios which differ with respect to network bandwidth as well as task and network complexity.

Knowledge-Based Systems

2011 ◽  
pp. 989-995 ◽  
Author(s):  
Adrian A. Hopgood
Keyword(s):  
Computational Intelligence ◽  
Early Research ◽  
Computer Hardware ◽  
Case Based Reasoning ◽  
Major Theme ◽  
Knowledge Based Systems ◽  
Rule Based ◽  
Knowledge Based ◽  
Explicit Representations ◽  
Case Based

The tools of artificial intelligence (AI) can be divided into two broad types: knowledge-based systems (KBSs) and computational intelligence (CI). KBSs use explicit representations of knowledge in the form of words and symbols. This explicit representation makes the knowledge more easily read and understood by a human than the numerically derived implicit models in computational intelligence. KBSs include techniques such as rule-based, modelbased, and case-based reasoning. They were among the first forms of investigation into AI and remain a major theme. Early research focused on specialist applications in areas such as chemistry, medicine, and computer hardware. These early successes generated great optimism in AI, but more broad-based representations of human intelligence have remained difficult to achieve (Hopgood, 2003; Hopgood, 2005).

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