The motivation of Grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, an efficient Grid scheduling System is an essential part of the Grid. Rather than covering the whole Grid scheduling area, this survey provides a review of the subject mainly from the perspective of scheduling algorithms. In this review, th...

Automated scoring of student essays is increasingly used to reduce manual grading effort. State-of-the-art approaches use supervised machine learning which makes it complicated to transfer a system trained on one task to another. We investigate which currently used features are task-independent and evaluate their transferability on English and German datasets. We find that, by using our task-in...

We propose a new task-specification language for Markov decision processes that is designed to be an improvement over reward functions by being environment independent. The language is a variant of Linear Temporal Logic (LTL) that is extended to probabilistic specifications in a way that permits approximations to be learned in finite time. We provide several small environments that demonstrate ...

For most reinforcement learning approaches, the learning is performed by maximizing an accumulative reward that is expectedly and manually defined for specific tasks. However, in real world, rewards are emergent phenomena from the complex interactions between agents and environments. In this paper, we propose an implicit generic reward model for reinforcement learning. Unlike those rewards that...

class LearningModule { -methods -process incoming date from the perceptual system and update episodic memory { } } C.3.11 EpisodicMemory Recall that in GTLF, episodic memory stores association data, perceptual selection data, and perceptual categorisation data. In our implementation, only the first two were catered for.

Past approaches for solving MDPs have several weaknesses: 1) Decision-theoretic computation over the state space can yield optimal results but scales poorly. 2) Value-function approximation typically requires human-specified basis functions and has not been shown successful on nominal (“discrete”) domains such as those in the ICAPS planning competitions. 3) Replanning by applying a classical pl...

Although there have been regular improvements in speech recognition technology over the past decade, speech recognition is far from being a solved problem. Recognition systems are usually tuned to a particular task and porting the system to a new task (or language) is both time-consuming and expensive. In this paper, issues in speech recognizer portability are addressed through the development ...

This study set out to explore the impact of three levels of task planning on the accuracy of task-based oral performance with narrative task types among sixty Iranian sophomores majoring in Teaching English as a Foreign Language (TEFL) at Islamic Azad University – Tabriz Branch. It was hypothesized that simultaneous pre-task/on-line planning would lead to more accurate performance. To test the ...

This paper presents a human-machine dialogue model in the field of task-oriented dialogues. The originality of this model resides in the clear separation of dialogue knowledge from task knowledge in order to facilitate for the modeling of dialogue strategies and the maintenance of dialogue coherence. These two aspects are crucial in the field of oral dialogues with a machine considering the cur...

In the present study, two different models of task complexity; namely, limited attentional capacity model and cognition hypothesis were examined. To this end, the manipulation of cognitive task complexity along +/- single task dimension on Iranian EFL learners’ production in terms of fluency was explored. Based on the results of the writing test of TOFEL (2004), 48 learners were selected as the...