The prisoner's dilemma has become the leading paradigm to explain the evolution of cooperation among selfish individuals. Here, we present an adaptive strategy that implements new mechanisms to process information about past encounters. The history of moves is summarized in an internal state which then determines the subsequent move. This enables the strategy to adjust its decisions to the char...

In this paper, we explore the use of genetic algorithms (GAs) to construct a system called GABIL that continually learns and refines concept classification rules from its interaction with the environment. The performance of this system is compared with that of two other concept learners (NEWGEM and C4.5) on a suite of target concepts. From this comparison, we identify strategies responsible for...

Computer games are an increasingly popular form of entertainment. Typically, the quality of AI opponents in computer games leaves a lot to be desired, which poses many attractive challenges for AI researchers. In this respect, Turn-based Strategy (TBS) games are of particular interest. These games are focussed on high-level decision making, rather than low-level behavioural actions. Moreover, t...

In this paper we develop a modified MRAC strategy for use on plants with unmodelled high frequency dynamics. The MRAC strategy is made up of two parts, an adaptive control part and a fixed gain control part. The adaptive algorithm uses a combination of low and high pass filters such that the frequency range for the adaptive part of the strategy is limited. This reduces adaptation to unexpected ...

This paper presents an adaptive tit-for-tat strategy and a study of its behavior in spatial IPD games. The adaptive tit-for-tat strategy is shown elsewhere to demonstrate high performance in IPD tournaments or individual IPD games with other types of strategies, and obtains higher scores than the pure tit-for-tat strategy. In spatial IPD games, the strategy exhibits stability and resistance to ...

In this paper, we presented a routing strategy for requests in unstructured peer-to-peer networks. The strategy is based on the adaptive routing Q-routing. The strategy uses reinforcement learning to estimate the cost of routing a request. Such a strategy is scalable only if the routing indices are of reasonable size. We proposed and comparatively evaluated three methods for the pruning for the...

The paper describes the strategy for efficient solving of difficult inverse problems, utilizing Finite Element Method (FEM) as a direct problem solver. The strategy consists of finding an optimal balance between the accuracy of global optimization method and the accuracy of an hp-adaptive FEM used for the multiple solving of the direct problem. The crucial relation among errors was found for th...

Memetic algorithms(MAs) represent one of the promising areas of evolutionary algorithms. However, there are many issues to be solved to design a robust MA. In this paper, we introduce an adaptive memetic algorithm, named GADEDHC, which combines a genetic algorithm and a differential evolution algorithm as global search methods with a directional hill climbing algorithm as local search method. I...

The Biomarker Strategy Design has been proposed for trials assessing the value of a biomarker in guiding treatment in oncology. In such trials, patients are randomised to a control arm, receiving the standard chemotherapy treatment, or a biomarker-directed treatment arm, when biomarker status is used to guide treatment. The randomised groups are then compared, often using a noninferiority test ...