Searching for surprise

Inspired by the notion of surprise for unconventional discovery in computational creativity, we introduce a general search algorithm we name surprise search. Surprise search is grounded in the divergent search paradigm and is fabricated within the principles of metaheuristic (evolutionary) search. The algorithm mimics the self-surprise cognitive process of creativity and equips computational creators with the ability to search for outcomes that deviate from the algorithm’s expected behavior. The predictive model of expected outcomes is based on historical trails of where the search has been and some local information about the search space. We showcase the basic steps of the algorithm via a problem solving (maze navigation) and a generative art task. What distinguishes surprise search from other forms of divergent search, such as the search for novelty, is its ability to diverge not from earlier and seen outcomes but rather from predicted and unseen points in the creative domain considered.This work has been supported in part by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).peer-reviewe

Constructing narrative using a generative model and continuous action policies

This paper proposes a method for learning how to generate narrative by recombining sentences from a previous collection. Given a corpus of story events categorised into 9 topics, we approximate a deep reinforcement learning agent policy to recombine them in order to satisfy narrative structure. We also propose an evaluation of such a system. The evaluation is based on coherence, interest, and topic, in order to figure how much sense the generated stories make, how interesting they are, and examine whether new narrative topics can emerge

Boosting computational creativity with human interaction in mixed-initiative co-creation tasks

Research in computational creativity often focuses on autonomously creative systems, which incorporate creative processes and result in creative outcomes. However, the integration of artificially intelligent processes in human-computer interaction tools necessitates that we identify how computational creativity can be shaped and ultimately enhanced by human intervention. This paper attempts to connect mixed-initiative design with established theories of computational creativity, and adapt the latter to accommodate a human initiative impacting computationally creative processes and outcomes. Several case studies of mixed-initiative tools for design and play are used to corroborate the arguments in this paper.peer-reviewe

Artificial life meets computational creativity?

I review the history of work in Artificial Life on the problem of the open-ended evolutionary growth of complexity in computational worlds. This is then put into the context of evolutionary epistemology and human creativity

Framing tension for game generation

Emotional progression in narratives is carefully structured by human authors to create unexpected and exciting situations, often culminating in a climactic moment. This paper explores how an autonomous computational designer can create frames of tension which guide the procedural creation of levels and their soundscapes in a digital horror game. Using narrative concepts, the autonomous designer can describe an intended experience that the automated level generator must adhere to. The level generator interprets this intent, bound by the possibilities and constraints of the game. The tension of the generated level guides the allocation of sounds in the level, using a crowdsourced model of tension.peer-reviewe

The longer term value of creativity judgements in computational creativity

During research to develop the Standardised Procedure for Evaluating Creative Systems (SPECS) methodology for evaluat- ing the creativity of ‘creative’ systems, in 2011 an evaluation case study was carried out. The case study investigated how we can make a ‘snapshot’ decision, in a short space of time, on the creativity of systems in various domains. The systems to be evaluated were presented at the International Computational Creativity Conference in 2011. Evaluation was performed by people whose domain expertise ranges from expert to novice, depending on the system. The SPECS methodology was used for evaluation, and was compared to two other creativity evaluation methods (Ritchie’s criteria and Colton’s Creative Tripod) and to results from surveying people’s opinion on the creativity of the systems under investigation. Here, we revisit those results, considering them in the context of what these systems have contributed to computational creativity development. Five years on, we now have data on how influential these systems were within computational creativity, and to what extent the work in these systems has influenced further developments in computational creativity research. This paper investigates whether the evaluations of creativity of these systems have been helpful in predicting which systems will be more influential in computational creativity (as measured by paper citations and further development within later computational systems). While a direct correlation between evaluative results and longer term impact is not discovered (and perhaps too simplistic an aim, given the factors at play in determining research impact), some interesting alignments are noted between the 2011 results and the impact of papers five years on

Computational Creativity

In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H. Yokota (Eds.), Springer 2011Understanding brain processes behind creativity and modeling them using computational means is one of the grand challenges for systems biology. Computational creativity is a new field, inspired by cognitive psychology and neuroscience. In many respects human-level intelligence is far beyond what artificial intelligence can provide now, especially in regard to the high-level functions, involving thinking, reasoning, planning and the use of language. Intuition, insight, imagery and creativity are important aspects of all these functions