This dissertation is an attempt of expanding the domain of game theory into the sphere of evolving, potentially non-equilibrium systems. We especially focus our attention on studying the effects of local interactions, using automata networks as a modelling tool.

Commercial games possess various methods of game balancing. Each of them modifies the game’s entertainment value for players of different skill levels. This paper deals with one of them, viz. a way of automatically adapting a game’s balance which is based on the theory of incongruity. We tested our approach on a group of subjects, who played a game with three difficulty settings. The idea is to...

The generalization of the problem of adaptive competition, known as the minority game, to the case of K possible choices for each player, is addressed, and applied to a system of interacting perceptrons with input and output units of a type of K-state Potts spins. An optimal solution of this minority game, as well as the dynamic evolution of the adaptive strategies of the players, are solved an...

Recent years have seen growing interest in intelligent game-based learning environments that simultaneously provide intelligent tutoring systems’ adaptive pedagogical functionalities and promote learners’ engagement through the rich narratives and situated challenges of games. A key research question posed by game-based learning is how to deliver effective adaptive scaffolding to support tailor...

Ultimate Game serves for extensive studies of various aspects of human decision making. The current paper contribute to them by designing proposer optimising its policy using Markov-decision-process (MDP) framework combined with recursive Bayesian learning of responder’s model. Its foreseen use: i) standardises experimental conditions for studying rationality and emotion-influenced decision mak...

We propose a new and simple adaptive procedure for playing a game: ‘‘regret-matching.’’ In this procedure, players may depart from their current play with probabilities that are proportional to measures of regret for not having used other strategies in the past. It is shown that our adaptive procedure guarantees that, with probability one, the empirical distributions of play converge to the set...

The design and analysis of an adaptive strategy for N-person averaged constrained stochastic repeated game are addressed. Each player is modeled by a stochastic variable-structure learning automaton. Some constraints are imposed on some functions of the probabilities governing the selection of the player's actions. After each stage, the payoff to each player as well as the constraints are rando...

In game-based learning, adaptive scaffolding can enhance the learning of domain-specific skills, known as first-order scaffolding, and self-regulatory second-order scaffolding. To design we need indicators that identify opportunities. Therefore investigated how performance self-regulation relate to overall game in a medical emergency simulation game. These have potential guide respectively. Twe...

The traditional approach to implementing interactions between a player character (PC) and objects in computer games is to write scripts in a procedural scripting language. These scripts are usually so complex that they must be written by a computer programmer rather than by the author of the game story. This interruption in the game story authoring process has two distinct disadvantages: it inc...

Pedagogy and the way children learn are changing rapidly with the introduction of widely accessible computer technologies, from mobile apps to interactive educational games. Digital games have the capacity to embed many learning supports using the widely accredited VARK (visual, auditory, reading, and kinaesthetic) learning style. In this paper, we present a mathematics educational game MathRun...