In this paper, we discuss obstacles for building intelligent parallel compilers and directions for improvements. A framework for the systematic program analysis and intelligent program restructuring control is presented. Methodologies for applying this framework to build optimizing parallel compilers, including heuristic-guided state-space search and planning, machine knowledge manipulation, sy...

An emotionally intelligent tutoring system should be able to taking into account relevant aspects of the mental state of the student when providing feedback. The student’s facial expressions, put in context, could provide cues with respect to this state. We discuss the analysis of the facial expression displayed by students interacting with an Intelligent Tutoring System and our attempts to rel...

Deep machine learning and reinforcement learning are two complementing fields within the study of intelligent systems. When combined, it is argued that they offer a promising path for achieving artificial general intelligence (AGI). This chapter outlines the concepts facilitating such merger of technologies and motivates a framework for building scalable intelligent machines. The prospect of ut...

The EDULAN project focuses in the field of Computer Assisted Learning and aims to generalize and translate to web-oriented technologies some results obtained previously on the Intelligent Tutoring Systems area. Particularly, four complementary research lines are being considered: knowledge acquisition tools for building Intelligent Tutoring Systems; adaptative and Web-oriented teaching/learning...

EUNITE, the European Network on Intelligent Technologies for Smart Adaptive Systems started January 1, 2001. Its targets were to join forces within the area of Intelligent Technologies for better understanding of the potential of hybrid systems and to provide guidelines for exploiting their practical implementations and particularly, to foster synergies that contribute towards building Smart Ad...

The advance of computing technology has provided the means for building intelligent vehicle systems. Drowsy driver detection system is one of the potential applications of intelligent vehicle systems. Here we employ machine learning techniques to detect driver drowsiness. The system obtained 98% performance in predicting driver drowsiness. This is the highest prediction rate reported to date fo...

In building intelligent tutoring systems, it is critical to be able to understand and diagnose student responses in interactive problem solving. We present a novel application of the q-matrix method, an educational data mining technique, to the problem of analyzing formal proofs. Our results indicate that automated analysis of formal proof data can provide an intelligent tutoring system with us...

In this paper we present a method for the development of an Intelligent Agent to assist in the design of Local Area Networks (LAN), a sub-domain of systems integration domain. We define the problem of LAN configuration design and describe a multistrategy machine learning approach for building the intelligent agent which includes domain modeling, ontology creation and the teaching of the agent. ...

The detection of emotions in text is a key issue for the development of intelligent systems. As demonstrated by the Turing test, a machine cannot be considered really intelligent unless it is also capable of perceiving and expressing emotions. In this work we focus on building a knowledge base which merges Common Sense and affective knowledge and use dimensionality reduction to perform emotive ...

The difficulty of domain knowledge acquisition is one of the most sensible challenges of intelligent tutoring systems. Relying on domain experts and building domain models from scratch are not viable solutions. The ability to automatically extract domain knowledge from documents can contribute to overcome these difficulties. In this paper, we use machine learning and natural language processing...