Intelligent game-based learning environments integrate commercial game technologies with AI methods from intelligent tutoring systems and intelligent narrative technologies. This article introduces the Crystal Island intelligent game-based learning environment, which has been under development in the authors' laboratory for the past seven years. After presenting Crystal Island, the principal technical problems of intelligent game-based learning environments are discussed: narrative-centered tutorial planning, student affect recognition, student knowledge modeling, and student goal recognition. The burgeoning field of game-based learning has made significant advances, including theoretical developments (Gee 2007), as well as the creation of gamebased learning environments for a broad range of K-12 subjects (Habgood and Ainsworth 2011; Ketelhut et al. 2010; Warren, Dondlinger, and Barab 2008) and training objectives (Johnson 2010; Kim et al. 2009). Of particular note are the results of recent empirical studies demonstrating that in addition to game-based learning environments' potential for motivation, they can enable students to achieve learning gains in controlled laboratory settings (Habgood and Ainsworth 2011) as well as classroom settings (Ketelhut et al. 2010).

In this article, we focus on RoboCup robot soccer, and present its five current leagues, which address complementary scientific challenges through different robot and physical setups. Full details on the status of the RoboCup soccer leagues, including league history and past results, upcoming competitions, and detailed rules and specifications are available from the league homepages and wikis. At that time -- the mid 1990s -- there were very few effective mobile robots, and the Honda P2 humanoid robot had just been presented to the public for the first time. The RoboCup challenge, set as an adversarial game between teams of autonomous robots, was therefore a fascinating and exciting development as well as a reference problem for AI. RoboCup introduced three robot soccer leagues, Simulation, Small Size, and Middle Size, and organized its first competitions at the 1997 International Joint Conference on Artificial Intelligence, held in Nagoya, Japan.

Each political decision in fact implies some form of social reactions, it affects economic and financial aspects and has substantial environmental impacts. Improving decision making in this context could have a huge beneficial impact on all these aspects. There are a number of Artificial Intelligence techniques that could play an important role in improving the policy-making process such as decision support and optimization techniques, game theory, data and opinion mining and agent-based simulation. We outline here some potential use of AI technology as it emerged by the European Union (EU) EU FP7 project ePolicy: Engineering the Policy Making Life Cycle, and we identify some potential research challenges. They are extremely complex, occur in rapidly changing environments characterized by uncertainty, and involve conflicts among different interests.

The aim of the Angry Birds AI competition (AIBIRDS) is to build intelligent agents that can play new Angry Birds levels better than the best human players. This is surprisingly difficult for AI as it requires similar capabilities to what humans need for successfully interacting with the physical world, one of the grand challenges of AI. As such the competition offers a simplified and controlled environment for developing and testing the necessary AI technologies, a seamless integration of computer vision, machine learning, knowledge representation and reasoning, reasoning under uncertainty, planning, and heuristic search, among others. Over the past three years there have been significant improvements, but we are still a long way from reaching the ultimate aim, and thus, there are great opportunities for participants in this competition. The competition was initiated in 2012 by the authors of this report and is held in collocation with some of the major AI conferences such as the International Joint Conference on Artificial Intelligence in 2013 and again in 2015, and the European Conference on Artificial Intelligence conference in 2014.

A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, and bilateral and multilateral protocols. Two of the challenges that remain are how to develop argumentation-based negotiation agents that, in addition to making offers, can inform and argue to obtain an acceptable agreement for both parties; and how to create agents that can negotiate in a human fashion. Challenges lie in the complexity of the negotiation domain, in the strategies for bidding and accepting, for opponent modeling, and so on. Competitions have proved their value as useful and open benchmarking tools to evaluate and compare agents in a common setting (for example, the successful Annual Computer Poker Competition and the various Trading Agent Competitions).

Where Are the Semantics in the Semantic Web? The most widely accepted defining feature of the semantic web is machine-usable content. By this definition, the semantic web is already manifest in shopping agents that automatically access and use web content to find the lowest air fares or book prices. However, where are the semantics? Most people regard the semantic web as a vision, not a reality--so shopping agents should not "count."

Because perception-action systems are necessarily constrained by the physics of time and space, robotocists often assume they are best described using differential equations, a language that is specialized for describing the evolution of variables that represent physical quantities. However, when it comes to decision making, where the representations involved refer to goals, strategies, and preferences, AI offers a diverse range of formalisms to the modeler. However, the relationship between these two levels of representation--signal and symbol--are not well understood. If we are to achieve success in modeling intelligent physical agents, robotics and AI must reach a new consensus on how to integrate perception-action systems with systems designed for abstract reasoning. All our major AI laboratories had research programs in robotics in the late 1960s and early 1970s.

Conjectures are of great importance since they suggest useful lines of research. It is stunning that so many predictions in Turing's 1950 Mind paper were right. In the decades since that paper appeared, with its inspiring challenges, research in computer science, neuroscience, and the behavioral sciences has radically changed thinking about mental processes and communication, and the ways in which people use computers has evolved even more dramatically. Turing, were he writing now, might still replace "Can machines think?" with an operational challenge, but it is likely he would propose a very different test. This paper considers what that might be in light of Turing's paper and advances in the decades since it was written.

Recent years have witnessed significant progress in intelligent user interfaces. Emerging from the intersection of AI and human-computer interaction, research on intelligent user interfaces is experiencing a renaissance, both in the overall level of activity and in raw research achievements. Because intelligent user interfaces are designed to facilitate problem-solving activities where reasoning is shared between users and the machine, they are currently transitioning from the laboratory to applications in the workplace, home, and classroom. Most of these projects have been presented at the premiere forum for intelligent user interface (IUI) research, the International Conference on Intelligent User Interfaces. First, they describe projects that explore the boundaries of the man-machine interface.

Choosing an environment is an important decision for agent developers. A key issue in this decision is whether the environment will provide realistic problems for the agent to solve, in the sense that the problems are true to the issues that arise in addressing a particular research question. In addition to realism, other important issues include how tractable problems are that can be formulated in the environment, how easy agent performance can be measured, and whether the environment can be customized or extended for specific research questions. In the ideal environment, researchers can pose realistic but tractable problems to an agent, measure and evaluate its performance, and iteratively rework the environment to explore increasingly ambitious questions, all at a reasonable cost in time and effort. As might be expected, tradeoffs dominate the suitability of an environment; however, we have found that the modern graphic user interface offers a good balance among these tradeoffs.