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.

Data access control is a field that has been a subject of a lot of research for many years, which has resulted in many models being designed. Many of these models are deterministic in nature, following set rules to allow or deny access to a given user. These are sufficient in fairly static environments, but they fall short in dynamic and collaborative settings where permission needs may change or user attributes may be missing. Risk-based and probabilistic models were designed to mitigate some of these issues. These take a user profile to determine the risk associated with a particular transaction or fill in any missing attributes. However, they need to be maintained as new security threats emerge. It is argued in this paper that cognitive systems, as part of a more general Cognitive Driven Access Control approach, can close this gap by learning security threats on their own and enhancing the security of data in these environments. The benefits and considerations to be made when deploying cognitive systems are also discussed