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You can start writing your papers to EvoGAMES. Time is running!

20 Sep

November 1st is the date when you must submit your high-quality contributions to EvoGAMES 2017. And to the rest of Evo* conferences and tracks, of course.

As you know, the topics of interest are mainly focused on the applications of bio-inspired algorithms in games or related research lines. Namely, we are interested in:
– Computational Intelligence in video games
– Intelligent avatars and new forms of player interaction
– Player experience measurement and optimization
– Procedural content generation
– Human-like artificial adversaries and emotion modelling
– Authentic movement, believable multi-agent control
– Experimental methods for gameplay evaluation
– Evolutionary testing and debugging of games
– Adaptive and interactive narrative and cinematography
– Games related to social, economic, and financial simulations
– Adaptive educational, serious and/or social games
– General game intelligence (e.g. general purpose drop-n-play Non-Player Characters, NPCs)
– Monte-Carlo tree search (MCTS)
– Affective computing in Games

The Evo* event will be held in Amsterdam on April 2017, so you’ll better do a very good work to get there!

You’ll have a lot of space to describe your work, up to 16 pages.

As usual, the accepted submissions will be included in the proceedings of Evo* (LNCS), but this year, a selection of the best papers in EvoAPPS will be invited to submit an extended version a special issue of Memetic Computing journal.

See you at the Red Light District in Amsterdam! 😀

The story of their lives: Massive procedural generation of heroes’ journeys using evolved agent-based models and logical reasoning

5 Apr

Paper presented as talk at EvoGAMES 2016 in Porto (Portugal).

BY:
Ruben H. García-Ortega, Pablo García-Sånchez, Juan J. Merelo, Aranzazu San-Ginés, Angel Fernåndez-Cabezas

ABSTRACT:

The procedural generation of massive subplots and backstories in secondary characters that inhabit Open World videogames usually lead to stereotyped characters that act as a mere backdrop for the virtual world; however, many game designers claim that the stories can be very relevant for the player’s experience. For this reason we are looking for a methodology that improves the variability of the characters’ personality while enhancing the quality of their backstories following artistic or literary guidelines. In previous works, we used multi agent systems in order to obtain stochastic, but regulated, inter-relations that became backstories; later, we have used genetic algorithms to promote the appearance of high level behaviors inside them.
Our current work continues the previous research line and propose a three layered system (Evolutionary computation – Agent-Based Model – Logical Reasoner) that is applied to the promotion of the monomyth, commonly known as the hero’s journey, a social pattern that constantly appears in literature, films, and videogames. As far as we know, there is no previous attempt to model the monomyth as a logical theory, and no attempt to use the sub-solutions for narrating purposes. Moreover, this paper shows for the first time this multi-paradigm three-layered methodology to generate massive backstories. Different metrics have been tested in the experimental phase, from those that sum all the monomyth-related tropes to those that promote distribution of archetypes in the characters. Results confirm that the system can make the monomyth emerge and that the metric has to take into account facilitator predicates in order to guide the evolutionary process.

PRESENTATION:

Enjoy it!

Evolving Chess-like Games Using Relative Algorithm Performance Profiles

3 Apr

Paper presented as talk at EvoGAMES 2016 in Porto (Portugal).

BY:
Jakub Kowalski, Marek Szykula

ABSTRACT:

We deal with the problem of automatic generation of complete rules of an arbitrary game. This requires a generic and accurate evaluating function that is used to score games. Recently, the idea that game quality can be measured using differences in performance of various gameplaying algorithms of different strengths has been proposed; this is called Relative Algorithm Performance Profiles. We formalize this method into a generally application algorithm estimating game quality, according to some set of model games with properties that we want to reproduce.
We applied our method to evolve chess-like boardgames. The results show that we can obtain playable and balanced games of high quality.

PRESENTATION:

http://kot.rogacz.com/Science/Research/publications/EvoGAMES2016_p.pdf

Enjoy it!