Human collaboration with systems within the Computational Creativity (CC) field is often restricted to shallow interactions, where the creative processes, of systems and humans alike, are carried out in isolation, without any (or little) intervention from the user, and without any discussion about how the unfolding decisions are taking place. Fruitful co-creation requires a sustained ongoing interaction that can include discussions of ideas, comparisons to previous/other works, incremental improvements and revisions, etc. For these interactions, communication is an intrinsic factor. This means giving a voice to CC systems and enabling two-way communication channels between them and their users so that they can: explain their processes and decisions, support their ideas so that these are given serious consideration by their creative collaborators, and learn from these discussions to further improve their creative processes. For this, we propose a set of design principles for CC systems that aim at supporting greater cocreation and collaboration with their human collaborators.

Code generation is a promising new area for the automatic production of mechanics and systems in games. Generated code alone is not sufficient for inclusion in a rich, fully-designed game, however - it lacks context to bind the functionality of code to the metaphorical setting of the game. In this paper we explore potential solutions to this problem, both in terms of creative systems which co-operate with human content, and the possibility for contextual meaning in autonomous, human-free creative systems as well.

Procedural content generation is often seen simply as a means to generate stuff, elaborating on or rearranging abstract data types that describe levels or modular pieces of gameplay. Generating content which is situated in an understanding of the real-world is a much harder task; it requires access to large amounts of knowledge, and a good technique for parsing and using that knowledge. In this paper we describe A Rogue Dream, a game prototype which can generate new visual content and change its design based on an input word from the player at the start of the game. We describe the game and the tools it makes use of to do this, and use the game to discuss ways in which such techniques might enable unique kinds of gameplay or new directions for intelligent design tools.

Fluidic games blur the line between gameplay and game design, providing playful, non-technical, easy-to-use tools for users to design their own games. We introduce Wevva , our first fluidic game for iOS, which allows users to design and share their own games based on novel mechanics, entirely on a mobile device, with no need for programming skills. The development of such software is a significant challenge in user interface design, requiring a new design approach compared to the design of more traditional creative tools, and with much scope for mixed-initiative co-creation and other assistive AI technologies. Our aim with fluidic games is to democratise game design, allowing anyone and everyone to express themselves through the artform of digital games.

The invention of fictional ideas is often a central process in the creative production of artefacts such as poems, music, paintings and games. Currently, fictional ideation is being studied by the Computational Creativity community within the WHIM European project. The aim of WHIM is to develop the What-If Machine, a software system capable of inventing, evaluating and presenting fictional ideas with cultural value. In this paper we explore the potential applications of the What-If Machine in the context of games. Specifically, we propose ways in which the What-If Machine can be used as an assistant for the design of games, by providing ideas about characters, the environment, etc., as well as a creative system during gameplay, through interesting interactions with the player.

While the quality of computationally generated artefacts continues to improve, some people still find it difficult to accept software as being creative. To help address this issue, we introduce the notion of creativity theatre, whereby computational creativity systems demonstrate their creative behaviours, not only for the purpose of producing valuable artefacts, but also to heighten the sense that observers have of it being creative. We present an approach to this whereby an entirely separate AI system controls a casual creator app, which is normally used as a creativity support tool by people. We describe the ‘Can You See What I Can See?’ installation which performs such creativity theatre, and describe its operation in a recent open house event. Introduction and Motivation One of the accepted definitions of the field of Computational Creativity is given in (Colton and Wiggins 2012) as: The philosophy, science and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviours that unbiased observers would deem to be creative. It is fair to say that most creative AI systems do not explicitly exhibit behaviours at all. That is, the processing they undertake in creating artefacts is often behind the scenes, and behaviours are either reverse-engineered (or guessed at) when people evaluate how the software has made its creations, or are read from technical papers describing how the software works. This lack of easily explainable and discernible behaviours makes it difficult for non-experts to independently assess the contribution particular systems/projects make towards advancing the field according to the definition above. One way to partially improve the situation is for software to frame its work (Charnley, Pease, and Colton 2012) by outputting text which describes its motivations, how it makes artefacts, its evaluation of its products and processes, etc. A survey of framing in computational creativity research is given in (Cook et al. 2019). Another possibility is for creative software to more deliberately exhibit behaviours in a real-time fashion during its creative production. One way of achieving this is via anthropomorphisation, i.e., giving the software abilities which mimic human actions, even if they are not strictly required to create artefacts. For instance, in some installations, the Continuator music generation system (Pachet 2003) moves the keys of a real piano keyboard. In general, an advantage of embedding a creative AI system in a robotic platform is such anthropomorphisation, as well as a clear separation of the creative behaviours of the system from the behaviours of the media being employed. For example, in robotic painting projects such as those in (Lindemeier et al. 2015) and (Tresset and Leymarie 2013), elements such as the robotic arm and camera mimic the hands and eyes of people, and as such, we can project particular behaviours onto them. These behaviours are separate from those of the analogue media (paints, brushes, pens, paper, etc.) that the robots employ. Similarly, in the You Can’t Know my Mind installation (Colton et al. 2015), The Painting Fool software makes paint strokes on-screen via a simulation of a hand holding a paintbrush. This anthropomorphisation enables viewers to separate The Painting Fool’s decision making system from its non-photorealistic rendering system. In artistic situations, such human-like physicality (whether real or simulated) can help audiences to project more nuanced behaviours onto creative AI systems, e.g., curiosity in (Gemeinboeck and Saunders 2010). In projects like the ones described above, machines stage performances which, among other things, help express their creativity. To capture this notion, we introduce, motivate and explore below the notion of creativity theatre for such performances. We then describe a project where creative behaviours are explicitly foregrounded, with a separate AI system controlling a generative system. We conclude with a recap and details of future work, including an art installation based on the work presented here.

With the growing integration of smartphones into our daily lives, and their increased ease of use, mobile games have become highly popular across all demographics. People listen to music, play games or read the news while in transit or bridging gap times. While mobile gaming is gaining popularity, mobile expression of creativity is still in its early stages. We present here a new type of mobile app -- fluidic games -- and illustrate our iterative approach to their design. This new type of app seamlessly integrates exploration of the design space into the actual user experience of playing the game, and aims to enrich the user experience. To better illustrate the game domain and our approach, we discuss one specific fluidic game, which is available as a commercial product. We also briefly discuss open challenges such as player support and how generative techniques can aid the exploration of the game space further.

Model checking and simulation are powerful techniques for developing and verifying the design of reactive systems. Here we propose the use of a complementary technique – automated theory formation. In particular, we report on an experiment in which we used a general purpose automated theory formation tool, HR, to explore properties of a model written in Promela. Our use of HR is constrained by meta-knowledge about the model that is relevant to hazard analysis. Moreover, we argue that such meta-knowledge will enable us to explore how safety properties could be violated.

MOBAs represent a huge segment of online gaming and are growing as both an eSport and a casual genre. The natural starting point for AI researchers interested in MOBAs is to develop an AI to play the game better than a human - but MOBAs have many more challenges besides adversarial AI. In this paper we introduce the reader to the wider context of MOBA culture, propose a range of challenges faced by the community today, and posit concrete AI projects that can be undertaken to begin solving them.

We introduce fluidic games, a type of casual creator that blends game play and game design. Fluidic games have a core of built-in games that anchor a space of de- sign possibilities around them, and encourage players to alternate between playing specific games and play- ing with the design space. Our Gamika Technology platform supports fluidic games on mobile devices, and we have thus far built three of them. In doing so, we have found that even for simple games, fluidic games require computational creativity support. This takes several forms intended to keep design sessions play- ful and fast-moving, including automated game design used as a form of brainstorming, mixed-initiative co- creative design to ease design-space navigation, and au- tomated game playing to evaluate game dynamics. Fi- nally, we have exhibited this fluidic-games concept in three distinct cultural settings: a series of rapid game jams lasting 1–2 hours each, an in-progress semester- long enrichment course with a local school, and an art installation that foregrounds an autonomous version of the system exploring a fluidic game on its own, at least if the audience will allow it to do so.

Within the WHIM project, we study fictional ideation: processes for automatically inventing, assessing and presenting fictional ideas. Here we examine the foundational notion of the plausibility of fictional ideas, by performing an empirical study to surface the factors that affect judgements of plausibility. Our long term aim is to formalise a computational method which captures some intuitive notions of plausibility and can predict how certain types of people will assess the plausibility of certain types of fictional ideas. This paper constitutes a first firm step towards this aim.

We provide a proof of principle that novel and engaging mobile casual games with new aesthetics, game mechanics and player interactions can be designed and tested directly on the device for which they are intended. We describe the Gamika iOS application which includes generative art assets; a design interface enabling the making of physics-based casual games containing multiple levels with aspects ranging from Frogger-like to Asteroids-like and beyond; a configurable automated playtester which can give feedback on the playability of levels; and an automated fine-tuning engine which searches for level parameterisations that enable the game to pass a battery of tests, as evaluated by the auto-playtester. Each aspect of the implementation represents a baseline with much room for improvement, and we present some experimental results and describe how these will guide the future directions for Gamika.

We introduce a new artificial intelligence (AI) approach called, the 'Digital Synaptic Neural Substrate' (DSNS). It uses selected attributes from objects in various domains (e.g. chess problems, classical music, renowned artworks) and recombines them in such a way as to generate new attributes that can then, in principle, be used to create novel objects of creative value to humans relating to any one of the source domains. This allows some of the burden of creative content generation to be passed from humans to machines. The approach was tested in the domain of chess problem composition. We used it to automatically compose numerous sets of chess problems based on attributes extracted and recombined from chess problems and tournament games by humans, renowned paintings, computer-evolved abstract art, photographs of people, and classical music tracks. The quality of these generated chess problems was then assessed automatically using an existing and experimentally-validated computational chess aesthetics model. They were also assessed by human experts in the domain. The results suggest that attributes collected and recombined from chess and other domains using the DSNS approach can indeed be used to automatically generate chess problems of reasonably high aesthetic quality. In particular, a low quality chess source (i.e. tournament game sequences between weak players) used in combination with actual photographs of people was able to produce three-move chess problems of comparable quality or better to those generated using a high quality chess source (i.e. published compositions by human experts), and more efficiently as well. Why information from a foreign domain can be integrated and functional in this way remains an open question for now. The DSNS approach is, in principle, scalable and applicable to any domain in which objects have attributes that can be represented using real numbers.

Computers can paint and make discoveries – the challenge is to teach them to code, says AI researcher Simon Colton

Procedural content generation in games tends to target content that is abstract, dry and devoid of connection with the game’s meaning. This paper proposes merging user-driven content generation approaches with procedural content generation to create a new paradigm which we call Hybrid Procedural Content Generation. By replacing aspects of existing procedural generation techniques with humans, we can give rise to new kinds of game experiences.

The term serendipity describes a creative process that develops, in context, with the active participation of a creative agent, but not entirely within that agent's control. While a system cannot be made to perform serendipitously on demand, we argue that its $\mathit{serendipity\ potential}$ can be increased by means of a suitable system architecture and other design choices. We distil a unified description of serendipitous occurrences from historical theorisations of serendipity and creativity. This takes the form of a framework with six phases: $\mathit{perception}$, $\mathit{attention}$, $\mathit{interest}$, $\mathit{explanation}$, $\mathit{bridge}$, and $\mathit{valuation}$. We then use this framework to organise a survey of literature in cognitive science, philosophy, and computing, which yields practical definitions of the six phases, along with heuristics for implementation. We use the resulting model to evaluate the serendipity potential of four existing systems developed by others, and two systems previously developed by two of the authors. Most existing research that considers serendipity in a computing context deals with serendipity as a service; here we relate theories of serendipity to the development of autonomous systems and computational creativity practice. We argue that serendipity is not teleologically blind, and outline representative directions for future applications of our model. We conclude that it is feasible to equip computational systems with the potential for serendipity, and that this could be beneficial in varied computational creativity/AI applications, particularly those designed to operate responsively in real-world contexts.

The invention of fictional ideas (ideation) is often a central process in the creative production of artefacts such as poems, music and paintings, but has barely been studied in the Computational Creativity community. We present here three baseline approaches for automated fictional ideation, using methods which invert and alter facts from the ConceptNet and ReVerb databases, and perform bisociative discovery. For each method, we present a curation analysis, by calculating the proportion of ideas which pass a typicality evaluation. We further evaluate one ideation approach through a crowd- sourcing experiment in which participants were asked to rank ideas. The results from this study, and the baseline methods and methodologies presented here, constitute a firm basis on which to build more sophisticated models for automated ideation with evaluative capacity.

We describe ANGELINA-5, software capable of creating simple three-dimensional games autonomously. To the best of our knowledge, this is the first system which creates complete games in 3D. We summarise the history of the ANGELINA project so far, describe the architecture of the latest version, and give details of its participation in Ludum Dare, a game design competition. This is the first time that a piece of software has entered a videogame design contest for human designers, and represents a step forward for automated videogame design and computational creativity.

We argue that the notion of creativity in a person or software is a secondary and essentially contested concept. Hence, in Computational Creativity research – where we aim to build software taken seriously as independently creative – understanding the roles people take as process observer and product consumer is paramount. Depending on the domain, there can be a natural bias against software created artefacts, and Computational Creativity researchers have exacerbated this situation through Turing-style comparison tests. Framing this as a modified Chinese Room experiment, We propose two remedies to the situation. These involve software accounting for its decisions, actions and products, and taking the radical step of thinking of computer generated artefacts as fundamentally different to their human-produced counterparts. We use two case studies, where people interact with an automated painter and with computer-generated videogames, to highlight the observer issues we raise, and to demonstrate partial implementations of our remedies.

Automated Theory Formation is a hybrid AI technique which has been implemented in two scientific discovery systems, HR1 and HR2, both of which have been used successfully in various applications. We describe here the latest iteration in the HR series, in terms of the lessons learned from the successes and failures of the previous versions, and how these lessons have informed our design choices and the implementation details of the new version. We also present two case studies: a synthetic domain mirroring an aspect of medical diagnosis, and invariant discovery in formal methods. In each case, we compare HR3 with HR2 to highlight various improvements in the new version.