Real-time strategy games offer a wide variety of fundamental AI research challenges. Most
of these challenges have applications outside the game domain. This paper provides a …

Owing to the complexity involved in training an agent in a real-time environment, eg, using
the Internet of Things (IoT), reinforcement learning (RL) using a deep neural network, ie …

Reinforcement Learning approach called MCRL. MCRL is applied in different domains to
construct context-aware model for mobile computing. For mobile devices, we present MCRL …

As the number of IoT devices grow rapidly, and soon to exceed 40 billion, security
challenges grow rapidly as well. One challenge proven to wreak havoc in the past few years …

Inventory control problem is faced by companies on a daily basis to optimise the supply
chain process and for predicting the optimal pricing for the item sales or for providing …

This paper considers a non-cooperative real-time strategy game between two teams; each
has multiple homogeneous players with identical capabilities. In particular, the first team …

This paper addresses the inventory control problem to predict the demand variations in real-
time. The work presents a novel solution using a reinforcement learning model that …

The security threats to mobile devices are growing exponentially as the degree of
sophistication for these smart devices increase. Mobile devices are not only used for phone …

This work presents a novel approach, socalled RBCM, in modeling a domain to construct
contextaware model for mobile computing. RBCM is based on a multivariate regression …

Abstract Highly capable Artificial Intelligences (AI) have been created for board games such
as Go and Chess. Players of these games can play against a computerised opponent at the …