Use of Artificial Intelligence and Machine Learning in Games

With modern video games surpassing every set of expectations in terms of graphics, game play, mechanics and hardware support, Artificial Intelligence in video games has also come a long way, from when it was first implemented in 1951. Although every set of games has an AI unique to itself, many of the algorithms are now developed such that they can be implemented in various games without any major changes in coding. But this could lead to the players exploiting AI in a single game to break the other games as well. Though this could be easily fixed by changing some minor fragments of algorithms, it would very well be an efficient way of developing complex AI for many games at once. This paper focuses on providing a cost-efficient way to implement AI algorithms that would benefit most of the upcoming and future games that will depend on AI to make themselves more dynamic to the players. This is done by taking the examples of various AI algorithms implemented in games like Pacman, Dota2, Tom Clancy's- The Division and many more.

A Survey of Planning and Learning in Games

In general, games pose interesting and complex problems for the implementation of intelligent agents and are a popular domain in the study of artificial intelligence. In fact, games have been at the center of some of the most well-known achievements in artificial intelligence. From classical board games such as chess, checkers, backgammon and Go, to video games such as Dota 2 and StarCraft II, artificial intelligence research has devised computer programs that can play at the level of a human master and even at a human world champion level. Planning and learning, two well-known and successful paradigms of artificial intelligence, have greatly contributed to these achievements. Although representing distinct approaches, planning and learning try to solve similar problems and share some similarities. They can even complement each other. This has led to research on methodologies to combine the strengths of both approaches to derive better solutions. This paper presents a survey of the multiple methodologies that have been proposed to integrate planning and learning in the context of games. In order to provide a richer contextualization, the paper also presents learning and planning techniques commonly used in games, both in terms of their theoretical foundations and applications.

Learning Theory and Heterogeneous Play in a Signaling-Game Experiment

We study the effect of how types are assigned to participants in a signaling-game experiment. The sender has two actions, In and Out, and two types. In one treatment, types are i.i.d. in every period, and senders gather experience with both types. In the other, types are assigned once-and-for-all, and feedback is type specific. The theory of learning in games predicts that the non-Nash but self-confirming equilibrium where some fraction of types play Out can persist in the fixed-type treatment but not when types are i.i.d. Our results confirm that more senders do play Out in the fixed-type treatment. (JEL C92, D82, D83)

ALF - a Framework for Evaluating Accelerated Learning in Industry

Games have long been considered as a means to support effective learning, motivate learners and accelerate their learning. Several successful studies using game-based learning are reported in the literature. However, there appears to be a research gap on systematically evaluating accelerated learning in game environments. The main research question we address in this paper is how can we evaluate accelerated learning in game-based learning environments? The main contribution of this paper will be a framework for evaluating accelerated learning in games (ALF). We will illustrate the use of this framework by describing studies conducted in the Norwegian industrial project ALTT (Accelerate Learning Through Technology), aimed at capacity building in the aluminium industry, where we have co-designed a game for accelerating learning about the electrolysis process for extracting aluminium and heat balance in the aluminium production cells.