This paper discusses emerging creative practices that involve interacting with generative computational systems, and the effect of such cybernetic interactions on our conceptions of creativity and agency. As computing systems have become more powerful in recent years, real time interaction with intelligent computational processes and models has emerged as a basis for innovative creative practices. Examples of these practices include interactive digital media installations, generative art works, live coding performances, virtual theatre, interactive cinema, and adaptive processes in computer games. In these types of activities computational systems have assumed a significant level of agency, or autonomy, that provoke questions about shared authorship and originality that are redefining our relationship with technologies and prompting new questions about human capabilities, values and the meaning of productive activities.

Beyond the goal of refining system design to the needs and tastes of users, user evaluation of interactive music systems offers a method of examining the nature of musical creativity as understood by its human practitioners. In the case of improvising music systems, user study and evaluation of a system’s ability to improvise may be useful in the ethnomusicological study of musical interaction in contemporary improvised music. A survey of preliminary findings based on the interactions of an improvising system, Maxine, with several improvisers is discussed, with results suggesting methodological reconfigurations of the purpose and goals of evaluating of interactive musical metacreations.

In recent years, the computerization of society has opened the door to the automation of information processes. Artificial intelligence, a subfield of computer sciences, has been tremendously successful at endowing machines with autonomous and proactive behaviors to achieve tasks that rely on intelligence when done by humans. As a result, machines are everywhere: omnipresent and unavoidable. Computational creativity is a new and fast growing field that is exploring the automation of creative processes. It investigates creativity as it is (striving to understand and simulate human creativity) as well as creativity as it could be (processes that we know humans to be incapable of, at least without machines).

Building complete interactive narrative systems is hard. Building systems that are satisfying for naïve users is especially hard since small deficiencies in component technologies can easily destroy the experience for a user. In this paper I argue that we can ameliorate some of these technical limitations through careful choice of genre and style, and discuss a number of properties of farce that make it a particularly attractive choice. Then I will describe work in progress on Punch and Judy AI Playset, a system that allows users to explore possible narratives in the Punch and Judy story world.

Most systems for generating cinematic shot sequences for virtual environments focus on the low-level problems of camera placement. While this approach will create a sequence of camera shots which film individual events in a virtual environment, it does not account for the high-level effects shot sequences have on viewer inferences. There are systems which are based on well known cinematography principles such as the rule of thirds and other framing principals, however these usually utilize schemas or predefined shots and do not reason about the high level cognitive effects on the viewer. In this paper a system is proposed which can reason directly about these high-level cognitive and narrative effects of a shot sequence on the viewer’s mental state.

Characters are vital to large video game worlds as they bring a sense of life to the world. However, background characters are known to rarely exhibit any sign of motivated behavior or emotional state. We want to change this by assigning these characters emotions that can be identified through their non-verbal behavior. We feel the addition of emotion will allow players to feel more connected to the game world and make the game world more believable. This paper presents the results of an experiment to test two ways of conveying emotion: 1) through a character's gait and 2) through a character's interactions with the game world. Results from the experiment suggest that a combination of gait and interactions is the most effective method to convey emotion.

We describe ANGELINA3, a system that can automatically develop games along a defined theme, by selecting appropriate multimedia content from a variety of sources and incorporating it into a game's design. We discuss these capabilities in the context of the FACE model for assessing progress in the building of creative systems, and discuss how ANGELINA3 can be improved through further work.

Automated sports commentary is a form of automated narrative. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We introduce a system called the Sports Commentary Recommendation System (SCoReS) that can automatically suggest stories for commentators to tell during games. Through several user studies, we compared commentary using SCoReS to three other types of commentary and show that SCoReS adds significantly to the broadcast across several enjoyment metrics. We also collected interview data from professional sports commentators who positively evaluated a demonstration of the system. We conclude that SCoReS can be a useful broadcast tool, effective at selecting stories that add to the enjoyment and watchability of sports. SCoReS is a step toward automating sports commentary and, thus, automating narrative.

In this work, we propose Answer-Set Programming (ASP) as a tool for rapid prototyping of dynamic programming algorithms based on tree decompositions. In fact, many such algorithms have been designed, but only a few of them found their way into implementation. The main obstacle is the lack of easy-to-use systems which (i) take care of building a tree decomposition and (ii) provide an interface for declarative specifications of dynamic programming algorithms. In this paper, we present D-FLAT, a novel tool that relieves the user of having to handle all the technical details concerned with parsing, tree decomposition, the handling of data structures, etc. Instead, it is only the dynamic programming algorithm itself which has to be specified in the ASP language. D-FLAT employs an ASP solver in order to compute the local solutions in the dynamic programming algorithm. In the paper, we give a few examples illustrating the use of D-FLAT and describe the main features of the system. Moreover, we report experiments which show that ASPbased D-FLAT encodings for some problems outperform monolithic ASP encodings on instances of small treewidth. To appear in Theory and Practice of Logic Programming (TPLP).

As an important tool for information filtering in the era of socialized web, recommender systems have witnessed rapid development in the last decade. As benefited from the better interpretability, neighborhood-based collaborative filtering techniques, such as item-based collaborative filtering adopted by Amazon, have gained a great success in many practical recommender systems. However, the neighborhood-based collaborative filtering method suffers from the rating bound problem, i.e., the rating on a target item that this method estimates is bounded by the observed ratings of its all neighboring items. Therefore, it cannot accurately estimate the unobserved rating on a target item, if its ground truth rating is actually higher (lower) than the highest (lowest) rating over all items in its neighborhood. In this paper, we address this problem by formalizing rating estimation as a task of recovering a scalar rating function. With a linearity assumption, we infer all the ratings by optimizing the low-order norm, e.g., the $l_1/2$-norm, of the second derivative of the target scalar function, while remaining its observed ratings unchanged. Experimental results on three real datasets, namely Douban, Goodreads and MovieLens, demonstrate that the proposed approach can well overcome the rating bound problem. Particularly, it can significantly improve the accuracy of rating estimation by 37% than the conventional neighborhood-based methods.