For delivering a precise Worst Case Execution Time (WCET), the WCET static analysers need the executable program and the target architecture. However, a prediction – even coarse – of the future WCET would be helpful at design stages where only the source code is available. We investigate the possibility of creating predictors of the WCET based on the C source code using machine-learning (work i...

Robust Reinforcement Learning aims to derive an optimal behavior that accounts for model uncertainty in dynamical systems. However, previous studies have shown that by considering the worst case scenario, robust policies can be overly conservative. Our soft-robust framework is an attempt to overcome this issue. In this paper, we present a novel Soft-Robust ActorCritic algorithm (SR-AC). It lear...

Recently, the application of the conventional rule based expert system for disease risk determination in medical domains has increased. However, a major limitation to the effectiveness of the rule based expert system approach is the sharp boundary problem that leads to underestimation or overestimation of boundary cases, which ultimately affects the accuracy of their recommendation. In this pap...

This paper discusses the development o f a web-based student enquiry system using techniques in case-based reasoning combined with advances in ontology. The system functions as an online enquiry front-desk to allow prospective students to fin d out if they are eligible for admission to a post-graduate course in an Australian University. Ontology is applied in the system to ensure explicit speci...

Regression Testing is the important testing approach basically used in case of versioning or any user oriented amendments in the software system. In such case instead of doing all test cases again only the selected test cases are being performed. In case of regression testing the sequence of test cases also changes based on the modified modules as well as errors in the software modules. In this...

Cooperation and learning are two ways in which an agent can improve its performance. Cooperative Multiagent Learning is a framework to analyze the tradeoff between cooperation and learning in multiagent systems. We focus on multiagent systems where individual agents are capable of solving problems and learning using CBR (Case-based Reasoning). We present several collaboration strategies for age...

In the following article, we show how a steadily growing set of textual documents can be managed within a case-based system. The presented project resides in the area of generating test speciications for a software system developed for the gas industry.

We present a novel algorithm for extracting a high-quality case base from raw data while preserving and sometimes improving the competence of case-based reasoning. We extend the framework of Smyth and Keane’s case-deletion policy with two additional features. First, we build a case base using a statistical distribution that is mined from the input data so that the case-base competence can be pr...

A case-based reasoning system for determining what colorants to use for producing a specific color of plastic was created. The selection of colorants needs to take many factors into consideration. A technique that involved fuzzy logic was used to compare the quality of the color match for each factor. The system has been in use for two years at a growing number of GE Plastics sites and has show...

Several adaptive learning systems are currently available. Nevertheless, most existing e-learning platforms lack efficient alignment to decision makers. Our approach is twofold. Firstly, we aim to integrate business goals in the selection process of learning concepts. Secondly, we make use of Case Based Reasoning (CBR) approach to learn from past experiences and construct effective adaptive sys...