introduction: patients with epilepsy can have impaired cognitive abilities. many factors contribute to this impairment, including the adverse effects of antiepileptic drugs like gabapentin (gbp). apart from anti-epilectic action, gabapentin is used to relieve ethanol withdrawal syndrome. because both gbp and ethanol act on gaba ergic system, the purpose of this study was to evaluate their effec...

Résumé : This paper addresses a relational reinforcement learning-like problem with general action-model relational learning and planning techniques. We propose an integrated system for both action model learning and action selection in the context of adaptive behavior of autonomous agents. Learning is incremental. It operates with relational representations and produces disjunctions of 1st ord...

This paper investigates to what extent learning methods are beneficial for the Lines of Action tournament program MIA. We focus on two components of the program: (1) the evaluation function and (2) the move ordering. Using temporal difference learning the evaluation function was improved by tuning the weights. We found substantial improvements for three weights. The move ordering was enhanced b...

The design of reinforcement learning solutions to many problems artificially constrain the action set available to an agent, in order to limit the exploration/sample complexity. While exploring, if an agent can discover new actions that can break through the constraints of its basic/atomic action set, then the quality of the learned decision policy could improve. On the flipside, considering al...

We describe a research framework for virtualizing documented physiotherapy instructions. Our approach bridges the gap between human understanding and the written manuals of instructions for physiotherapy. Techniques of Natural Language Processing involving semantic and spatial information processing are important in this approach. We have also explained the physiotherapy considerations that we ...

In this work, we look at the challenge of learning in an action game, Infinite Mario. Learning to play an action game can be divided into two distinct but related problems, learning an object-related behavior and selecting a primitive action. We propose a framework that allows for the use of reinforcement learning for both of these problems. We present promising results in some instances of the...