737 research outputs found
Learning the Designer's Preferences to Drive Evolution
This paper presents the Designer Preference Model, a data-driven solution
that pursues to learn from user generated data in a Quality-Diversity
Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the
user's design style to better assess the tool's procedurally generated content
with respect to that user's preferences. Through this approach, we aim for
increasing the user's agency over the generated content in a way that neither
stalls the user-tool reciprocal stimuli loop nor fatigues the user with
periodical suggestion handpicking. We describe the details of this novel
solution, as well as its implementation in the MI-CC tool the Evolutionary
Dungeon Designer. We present and discuss our findings out of the initial tests
carried out, spotting the open challenges for this combined line of research
that integrates MI-CC with Procedural Content Generation through Machine
Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European
Conference on the Applications of Evolutionary and bio-inspired Computation,
EvoApplications 202
Discovering Evolutionary Stepping Stones through Behavior Domination
Behavior domination is proposed as a tool for understanding and harnessing
the power of evolutionary systems to discover and exploit useful stepping
stones. Novelty search has shown promise in overcoming deception by collecting
diverse stepping stones, and several algorithms have been proposed that combine
novelty with a more traditional fitness measure to refocus search and help
novelty search scale to more complex domains. However, combinations of novelty
and fitness do not necessarily preserve the stepping stone discovery that
novelty search affords. In several existing methods, competition between
solutions can lead to an unintended loss of diversity. Behavior domination
defines a class of algorithms that avoid this problem, while inheriting
theoretical guarantees from multiobjective optimization. Several existing
algorithms are shown to be in this class, and a new algorithm is introduced
based on fast non-dominated sorting. Experimental results show that this
algorithm outperforms existing approaches in domains that contain useful
stepping stones, and its advantage is sustained with scale. The conclusion is
that behavior domination can help illuminate the complex dynamics of
behavior-driven search, and can thus lead to the design of more scalable and
robust algorithms.Comment: To Appear in Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO 2017
Monitoring of the primary drying of a lyophilization process in vials
An innovative and modular system (LyoMonitor) for monitoring the primary drying of a lyophilization process in vials is illustrated: it integrates some commercial devices (pressure gauges, moisture sensor and mass spectrometer), an innovative balance and a manometric temperature measurement system based on an improved algorithm (DPE) to estimate sublimating interface temperature and position, product temperature profile, heat and mass transfer coefficients. A soft-sensor using a multipoint wireless thermometer can also estimate the previous parameters in a large number of vials. The performances of the previous devices for the determination of the end of the primary drying are compared. Finally, all these sensors can be used for control purposes and for the optimization of the process recipe; the use of DPE in a control loop will be shown as an exampl
Data Adventures
This paper outlines a system for generating adventure games based on open data, and describes a sketch of the system im-plementation at its current state. The adventure game genre has been popular for a long time and differs significantly in design priorities from game genres which are commonly ad-dressed in PCG research. In order to create believable and engaging content, we use data from DBpedia to generate the game’s non-playable characters locations and plot, and OpenStreetMaps to create the game’s levels. 1
Using dates as contextual information for personalised cultural heritage experiences
We present semantics-based mechanisms that aim to promote reflection on cultural heritage by means of dates (historical events or annual commemorations), owing to their connections to a collection of items and to the visitors’ interests. We argue that links to specific dates can trigger curiosity, increase retention and guide visitors around the venue following new appealing narratives in subsequent visits. The proposal has been evaluated in a pilot study on the collection of the Archaeological Museum of Tripoli (Greece), for which a team of humanities experts wrote a set of diverse narratives about the exhibits. A year-round calendar was crafted so that certain narratives would be more or less relevant on any given day. Expanding on this calendar, personalised recommendations can be made by sorting out those relevant narratives according to personal events and interests recorded in the profiles of the target users. Evaluation of the associations by experts and potential museum visitors shows that the proposed approach can discover meaningful connections, while many others that are more incidental can still contribute to the intended cognitive phenomena
Playing with data : procedural generation of adventures from open data
This paper investigates how to generate simple adventure games using open data. We present a system that creates a plot for the player to follow based on associations between Wikipedia articles which link two given topics (in this case people) together. The Wikipedia articles are transformed into game objects (locations, NPCs and items) via constructive algorithms that also rely on geographical information from OpenStreetMaps and visual content from Wikimedia Commons. The different game objects generated in this fashion are linked together via clues which point to one another, while additional false clues and dead ends are added to increase the exploration value of the final adventure game. This information is presented to the user via a set of game screens and images. Inspired by the “Where in the World is Carmen Sandiego?” adventure game, the end result is a generator of chains of followable clues.peer-reviewe
Data adventures
This paper outlines a system for generating adventure games based on open data, and describes a sketch of the system implementation at its current state. The adventure game genre has been popular for a long time and differs signi cantly in design priorities from game genres which are commonly addressed in PCG research. In order to create believable and engaging content, we use data from DBpedia to generate the game's non-playable characters locations and plot, and OpenStreetMaps to create the game's levels.peer-reviewe
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