The workshops took place October 8-9, 2012, at Stanford University. This report contains summaries of the activities of those four workshops. With the advent of the BWAPI StarCraft programming interface, interest in real-time strategy (RTS) game AI has increased considerably. At the 2011 AIIDE conference, several papers on the subject were presented, ranging from build order planning, over state estimation, to plan recognition. In addition, a panel discussion on RTS game AI took place, the StarCraft competition was discussed, prizes were awarded, and two exhibition match replays were shown.

Neuromorphic, evolutionary, or fuzzylike systems have been developed by many research groups in the Spanish computer sciences. It is no surprise, then, that these naturegrounded efforts start to emerge, enriching the AI catalogue of research projects and publications and, eventually, leading to new directions of basic or applied research. In this article, we review the contribution of Melomics in computational creativity. In Spain there are 74 universities, many of which have computer science departments that host AIrelated research groups. AEPIA, the Spanish society for AI research, was founded in 1983 and has been vigorously promoting the advancement of AI since then. Along with several other societies and communities of interest, it promotes various periodic conferences and workshops. The Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council constitutes one of the flagships of local AI research. Ramón López de Mántaras, IIIA's renowned director, was one of the pioneers of AI in Spain, and he also was the recipient of the prestigious AAAI Englemore Award in 2011. Other researchers that have reached an outstanding position, and lead important research groups in Spain, include Antonio Bahamonde (University of Oviedo), Federico Barber (Polytechnic University of Madrid), Vicent Botti (Polytechnic University of Valencia), and Amparo Vila (University of Granada). This department, with more than one hundred faculty members, is organized in several research groups, three of which maintain active AI research lines. Melomics is a new approach in artificial creativity (for a perspective on this discipline, see the 2009 fall issue of AI Magazine). More specifically, it focuses on algorithmic composition and aims at the full automation of the composition process of professional music.

A computer program is presented that learns to play piano with "expression" and that even won an international computer piano performance contest. A superficial analysis of an expressive performance generated by the system seems to suggest creative musical abilities. After a critical discussion of the processes underlying this behavior, we abandon the question of whether the system is really creative and turn to the true motivation that drives this research: to use AI methods to investigate and better understand music performance as a human creative behavior. A number of recent and current results from our research are briefly presented that indicate that machines can give us interesting insights into such a complex creative behavior, even if they may not be creative themselves. A computer program is to play two piano pieces that it has never seen before in an "expressive" way (that is, by shaping tempo, timing, dynamics, and articulation in such a way that the performances sound "musical" or "human").

The article introduces the reader to a large interdisciplinary research project whose goal is to use AI to gain new insight into a complex artistic phenomenon. We study fundamental principles of expressive music performance by measuring performance aspects in large numbers of recordings by highly skilled musicians (concert pianists) and analyzing the data with state-of-the-art methods from areas such as machine learning, data mining, and data visualization. The article first introduces the general research questions that guide the project and then summarizes some of the most important results achieved to date, with an emphasis on the most recent and still rather speculative work. A broad view of the discovery process is given, from data acquisition through data visualization to inductive model building and pattern discovery, and it turns out that AI plays an important role in all stages of such an ambitious enterprise. Our current results show that it is possible for machines to make novel and interesting discoveries even in a domain such as music and that even if we might never find the "Horowitz Factor," AI can give us completely new insights into complex artistic behavior.

With so much music readily available, tools that help a user find new, interesting music that matches his or her taste become increasingly important. In this article we explore one such tool: music recommendation. We describe common music recommendation use cases such as finding new artists, finding others with similar listening tastes, and generating interesting music playlists. We describe the various approaches currently being explored by practitioners to satisfy these use cases. Finally, we show how results of three different music recommendation technologies compare when applied to the task of finding similar artists to a seed artist.

IAAI seeks out applications of artificial intelligence that either demonstrate new technology or use previously known technology in innovative ways. IAAI particularly seeks out examples of deployments of AI technology that tackle the problems of demonstrating value and planning for long-term deployment. The five articles we have selected for this special issue are extended versions of papers that appeared in the conference. Two of the articles are deployed applications that have already demonstrated practical value. The remaining three articles are particularly innovative emerging applications.

It was written not by a human being, but by my computer program EWI (an acronym for "experiments in writing intelligence"). EWI was fed the texts of two of Hofstadter's books--namely, Gödel, Escher, Bach (winner of the Pulitzer Prize for General Nonfiction in 1980) and Metamagical Themas--and then, following its code, EWI carefully analyzed these two books for their uniquely Hofstadterian stylistic elements and features, after which it recombined these stylistic elements in new fashions. EWI thereby came up with some 25 new and highly diverse "Hofstadter articles," one of which is given below, and the article is followed by a brief commentary about EWI and its output by Hofstadter himself. Actually, I should state up front that the wonderful sparkling dialogues of GEB, which are a substantial part of that book, were not used by EWI in generating any of the articles, because EWI is unfortunately not yet able to work with inputs that belong to different genres, such as chapters and dialogues. To combine stylistic aspects of two or more different genres of writing represents a very thorny challenge indeed.

If we want to understand an event in depth, from multiple perspectives, we need to aggregate multiple sources and understand the relations between them. However, current news aggregators do not offer this kind of functionality. As a step toward a solution, we propose DiversiNews, a real-time news aggregation and exploration platfom whose main feature is a novel set of controls that allow users to contrast reports of a selected event based on topical emphases, sentiment differences, and/or publisher geolocation. News events are presented in the form of a ranked list of articles pertaining to the event and an automatically generated summary. Both the ranking and the summary are interactive and respond in real time to user's change of controls.

AI technology was employed to present the right information efficiently to each reader and to reduce radically the workload of curators. The system went through three implementation cycles and processed more than 20 million news stories from about 12,000 Really Simple Syndication (RSS) feeds on more than 8000 topics organized by 160 curators for more than 600 registered readers. This article describes the approach, engineering, and AI technology of the system. It is hard to keep up on what matters. The limiting factor is not the amount of information available but our available attention (Simon 1971).

One of the world's most valuable start-ups got that way by using artificial intelligence to satisfy Chinese internet users' voracious appetite for news and entertainment. Every day, its smartphone app feeds 120 million people personalized streams of buzzy news stories, videos of dogs frolicking in snow, GIFs of traffic mishaps and listicles such as "The World's Ugliest Celebrities." Now the company is discovering the risks involved, under China's censorship regime, in giving the people exactly what they want. The makers of the popular news app Jinri Toutiao unveiled moves this week to allay rising concerns from the authorities. Last week, the Beijing bureau of China's top internet regulator accused Toutiao of "spreading pornographic and vulgar information" and "causing a negative impact on public opinion online," and it ordered that updates to several popular sections of the app be halted for 24 hours.