While the health benefits of regular physical activity are well-established, many people exercise much less than is recommended by established guidelines. Music has been shown to have a motivational effect that can encourage people to exercise more strenuously or for longer periods of time, but the determination of which songs should be provided to which exercisers is an unsolved problem. We pr...

Personalization according to specific requirements of an individual student is one of the most important features in adaptive educational systems. Considering learning and how to improve a student’s performance, these systems must know the way in which an individual student learns best. In this context, the current work outlines a new approach to automatically and dynamically discover students ...

Modeling the behavior of the dialogue management in the design of a spoken dialogue system using statistical methodologies is currently a growing research area. This paper presents a work on developing an adaptive learning approach to optimize dialogue strategy. At the core of our system is a method formalizing dialogue management as a sequential decision making under uncertainty whose underlyi...

Manually assigned keywords provide a valuable means for accessing large document collections. They can serve as a shallow document summary and enable more efficient retrieval and aggregation of information. In this paper we investigate keywords in the context of the Dutch Folktale Database, a large collection of stories including fairy tales, jokes and urban legends. We carry out a quantitative...

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

Reinforcement learning is an attractive method of machine learning. However, as the state space of a given problem increases, reinforcement learning becomes increasingly inefficient. Hierarchical reinforcement learning is one method of increasing the efficiency of reinforcement learning. It involves breaking the overall goal of a problem into a hierarchy subgoals, and then attempting to achieve...

Preference-based reinforcement learning has gained significant popularity over the years, but it is still unclear what exactly preference learning is and how it relates to other reinforcement learning tasks. In this paper, we present a general definition of preferences as well as some insight how these approaches compare to reinforcement learning, inverse reinforcement learning and other relate...

In this paper we investigate the use of reinforcement learning to address the multiagent coalition formation problem in dynamic, uncertain, real-time, and noisy environments. To adapt to the complex environmental factors, we equip each agent with the case-based reinforcement learning ability which is the integration of case-based reasoning and reinforcement learning. The agent can use case-base...

Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the ever-increasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to incr...

Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can prov...