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.

The goal of an interactive narrative system is to immerse users in a virtual world such that they believe that they are an integral part of an unfolding story and that their actions can significantly alter the direction or outcome of the story. In this article we review the ways in which artificial intelligence can be brought to bear on the creation of interactive narrative systems. We lay out the landscape of about 20 years of interactive narrative research and explore the successes as well as open research questions pertaining to the novel use of computational narrative intelligence in the pursuit of entertainment, education, and training. The prevalence of storytelling in human culture may be explained by the use of narrative as a cognitive tool for situated understanding (Gerrig 1993). This narrative intelligence -- the ability to organize experience into narrative form -- is central to the cognitive processes employed across a range of experiences, from entertainment to active learning.

Collaborative query routing is a new paradigm for web search that treats both established search engines and other publicly available indexes as intelligent peer agents in a search network. The approach makes it transparent for anyone to build his or her own (micro) search engine by integrating established web search services, desktop search, and topical crawling techniques. The challenge in this model is that each of these agents must learn about its environment--the existence, knowledge, diversity, reliability, and trustworthiness of other agents--by analyzing the queries received from and results exchanged with these other agents. We present the 6S peer network, which uses machine-learning techniques to learn about the changing query environment. We show that simple reinforcement learning algorithms are sufficient to detect and exploit semantic locality in the network, resulting in efficient routing and highquality search results.

These fields complement each other: Many statistical methods, particularly those for large data sets, rely on computation, but brute computing power is no substitute for statistical knowledge. Thus, we are seeing the development of intelligent systems for data analysis. To provide an international forum for the discussion of these topics, a series of symposia on IDA was started in 1995 (Liu 1996). In 1997, the Second International Symposium on Intelligent Data Analysis (IDA97) was held at Birkbeck College, University of London, on 4 to 6 August. Almost 130 people from 20 countries in 4 continents attended.