Effects of Anticipation in Individually Motivated Behaviour on Control and Survival in a Multi-Agent Scenario with Resource Constraints

This is an open access article distributed under the Creative Commons Attribution License CC BY 3.0 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Self-organization and survival are inextricably bound to an agent’s ability to control and anticipate its environment. Here we assess both skills when multiple agents compete for a scarce resource. Drawing on insights from psychology, microsociology and control theory, we examine how different assumptions about the behaviour of an agent’s peers in the anticipation process affect subjective control and survival strategies. To quantify control and drive behaviour, we use the recently developed information-theoretic quantity of empowerment with the principle of empowerment maximization. In two experiments involving extensive simulations, we show that agents develop risk-seeking, risk-averse and mixed strategies, which correspond to greedy, parsimonious and mixed behaviour. Although the principle of empowerment maximization is highly generic, the emerging strategies are consistent with what one would expect from rational individuals with dedicated utility models. Our results support empowerment maximization as a universal drive for guided self-organization in collective agent systemsPeer reviewedFinal Published versio

STATSREP-ML: Statistical Evaluation & Reporting Framework for Machine Learning Results

In this report, we present STATSREP-ML, which is an open-source solution for automating the process of evaluating machine-learning results. It calculates qualitative statistics, performs the appropriate tests and reports them in a comprehensive way. It largely, but not exclusively, relies on well-tested and robust statistics implementations in R, and uses the tests the machine-learning community largely agreed upon

Intrinsic Motivation in Computational Creativity Applied to Videogames

PhD thesisComputational creativity (CC) seeks to endow artificial systems with creativity. Although human creativity is known to be substantially driven by intrinsic motivation (IM), most CC systems are extrinsically motivated. This restricts their actual and perceived creativity and autonomy, and consequently their benefit to people. In this thesis, we demonstrate, via theoretical arguments and through applications in videogame AI, that computational intrinsic reward and models of IM can advance core CC goals. We introduce a definition of IM to contextualise related work. Via two systematic reviews, we develop typologies of the benefits and applications of intrinsic reward and IM models in CC and game AI. Our reviews highlight that related work is limited to few reward types and motivations, and we thus investigate the usage of empowerment, a little studied, information-theoretic intrinsic reward, in two novel models applied to game AI. We define coupled empowerment maximisation (CEM), a social IM model, to enable general co-creative agents that support or challenge their partner through emergent behaviours. Via two qualitative, observational vignette studies on a custom-made videogame, we explore CEM’s ability to drive general and believable companion and adversary non-player characters which respond creatively to changes in their abilities and the game world. We moreover propose to leverage intrinsic reward to estimate people’s experience of interactive artefacts in an autonomous fashion. We instantiate this proposal in empowerment-based player experience prediction (EBPXP) and apply it to videogame procedural content generation. By analysing think-aloud data from an experiential vignette study on a dedicated game, we identify several experiences that EBPXP could predict. Our typologies serve as inspiration and reference for CC and game AI researchers to harness the benefits of IM in their work. Our new models can increase the generality, autonomy and creativity of next-generation videogame AI, and of CC systems in other domains

Action Selection in the Creative Systems Framework

The Creative Systems Framework (CSF) formalises creativity as search through a space of concepts. As a formal account of Margaret Boden’s descriptive hierarchy of creativity, it is at the basis of multiple studies dealing with diverse aspects of Computational Creativity (CC) systems. However, the CSF at present neither formalises action nor action selection during search, limiting its use in analysing creative processes. We extend the CSF by explicitly modelling these missing components in the search space traversal function. We furthermore introduce a distinction between a concept and its material realisation as an artefact, and elaborate the action selection process to provide stopping criteria for creative search. Our extension, the Creative Action Selection Framework (CASF), is informed by previous studies in CC and draws on concepts from Markov Decision Processes (MDP). It allows us to describe a creative system as an agent selecting actions based on the value, validity and novelty of concepts and artefacts. The CASF brings the descriptive power of the CSF to a wider range of systems with more analytical depth.Peer reviewe

Generative Design in Minecraft: Chronicle Challenge

© 2016 ACC 2019We introduce the Chronicle Challenge as an optional addition to the Settlement Generation Challenge in Minecraft. One of the foci of the overall competition is adaptive procedural content generation (PCG), an arguably under-explored problem in computational creativity. In the base challenge, participants must generate new settlements that respond to and ideally interact with existing content in the world, such as the landscape or climate. The goal is to understand the underlying creative process, and to design better PCG systems. The Chronicle Challenge in particular focuses on the generation of a narrative based on the history of a generated settlement, expressed in natural language. We discuss the unique features of the Chronicle Challenge in comparison to other competitions, clarify the characteristics of a chronicle eligible for submission and describe the evaluation criteria. We furthermore draw on simulation-based approaches in computational storytelling as examples to how this challenge could be approached.Peer reviewe

Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities

We present a framework for automating generative deep learning with a specific focus on artistic applications. The framework provides opportunities to hand over creative responsibilities to a generative system as targets for automation. For the definition of targets, we adopt core concepts from automated machine learning and an analysis of generative deep learning pipelines, both in standard and artistic settings. To motivate the framework, we argue that automation aligns well with the goal of increasing the creative responsibility of a generative system, a central theme in computational creativity research. We understand automation as the challenge of granting a generative system more creative autonomy, by framing the interaction between the user and the system as a co-creative process. The development of the framework is informed by our analysis of the relationship between automation and creative autonomy. An illustrative example shows how the framework can give inspiration and guidance in the process of handing over creative responsibility

How Does Embodiment Affect the Human Perception of Computational Creativity? An Experimental Study Framework

Which factors influence the human assessment of creativity exhibited by a computational system is a core question of computational creativity (CC) research. Recently, the system’s embodiment has been put forward as such a factor, but empirical studies of its effect are lacking. To this end, we propose an experimental framework which isolates the effect of embodiment on the perception of creativity from its effect on creativity per se. We manipulate not only the system’s embodiment but also the human perception of creativity, which we factorise into the assessment of creativity, and the perceptual evidence that feeds into that assessment. We motivate the core framework with embodiment and perceptual evidence as independent and the creative process as a controlled variable, and we provide recommendations on measuring the assessment of creativity as a dependent variable. We propose three types of perceptual evidence with respect to the creative system, the creative process and the creative artefact, borrowing from the popular four perspectives on creativity. We hope the framework will inspire and guide others to study the human perception of embodied CC in a principled manner.Peer reviewe

How Does Embodiment Affect the Human Perception of Computational Creativity? An Experimental Study Framework

Which factors influence the human assessment of creativity exhibited by a computational system is a core question of computational creativity (CC) research. Recently, the system’s embodiment has been put forward as such a factor, but empirical studies of its effect are lacking. To this end, we propose an experimental framework which isolates the effect of embodiment on the perception of creativity from its effect on creativity per se. We manipulate not only the system’s embodiment but also the human perception of creativity, which we factorise into the assessment of creativity, and the perceptual evidence that feeds into that assessment. We motivate the core framework with embodiment and perceptual evidence as independent and the creative process as a controlled variable, and we provide recommendations on measuring the assessment of creativity as a dependent variable. We propose three types of perceptual evidence with respect to the creative system, the creative process and the creative artefact, borrowing from the popular four perspectives on creativity. We hope the framework will inspire and guide others to study the human perception of embodied CC in a principled manner.Peer reviewe