Traditional optimization solvers tend to start the search from scratch by assuming zero prior
knowledge about the task at hand. Generally speaking, the capabilities of solvers do not …

Knowledge transfer from optimized problems has emerged as a promising technique for
enhancing evolutionary search. However, most studies in this domain primarily concentrate …

Since it first appeared, differential evolution (DE), one of the most successful evolutionary
algorithms, has been studied by many researchers. Theoretical and empirical studies of the …

Artificial intelligence for digital games constitutes the implementation of a set of algorithms
and techniques from both traditional and modern artificial intelligence in order to provide …

Evolutionary sequential transfer optimization is a paradigm that leverages search
experience from solved source optimization tasks to accelerate the evolutionary search of a …

Current approaches to adaptive game AI typically require numerous trials to learn effective
behavior (ie, game adaptation is not rapid). In addition, game developers are concerned that …

The behavioral differences between expert and novice performance is a well-studied area in
training literature. Advances in technology have made it possible to trace players' actions …

Games are good test-beds to evaluate AI methodologies. In recent years, there has been a
vast amount of research dealing with real-time computer games other than the traditional …

The real-time strategy game StarCraft has become an important benchmark for AI research
as it poses a complex environment with numerous challenges. An important strategic aspect …

This paper illustrates how we create a software agent by employing FALCON, a self-
organizing neural network that performs reinforcement learning, to play a well-known first …