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Evolving Game-specific UCB Alternatives for General Video Game Playing

3 May

Interesting paper presented as a talk at EvoGAMES 2017 in Amsterdam (The Netherlands) by Ivan Bravi.

Co-authored by: Ahmed Khalifa , Christoffer Holmgard , Julian Togelius

ABSTRACT:

At the core of the most popular version of the Monte Carlo Tree Search (MCTS) algorithm is the UCB1 (Upper Confidence Bound) equation. This equation decides which node to explore next, and therefore shapes the behavior of the search process. If the UCB1 equation is replaced with another equation, the behavior of the MCTS algorithm changes, which might increase its performance on certain problems (and decrease it on others). In this paper, we use genetic programming to evolve replacements to the UCB1 equation targeted at playing individual games in the General Video Game AI (GVGAI) Framework. Each equation is evolved to maximize playing strength in a single game, but is then also tested on all other games in our test set. For every game included in the experiments, we found a UCB replacement that performs significantly better than standard UCB1. Additionally, evolved UCB replacements also tend to improve performance in some GVGAI games for which they are not evolved, showing that improvements generalize across games to clusters of games with similar game mechanics or algorithmic performance. Such an evolved portfolio of UCB variations could be useful for a hyper-heuristic game-playing agent, allowing it to select the most appropriate heuristics for particular games or problems in general.

PRESENTATION:

Ivan Bravi – EvoGAMES – Evolving UCT alternatives

LINK TO THE PAPER:

https://link.springer.com/chapter/10.1007/978-3-319-55849-3_26

http://julian.togelius.com/Bravi2017Evolving.pdf

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Automated Game Balancing in Ms PacMan and StarCraft using Evolutionary Algorithms

25 Apr

Incredible paper presented as a talk at EvoGAMES 2017 in Amsterdam (The Netherlands) by Mihail Morosan.

Co-authored by: Ricardo Poli.

ABSTRACT:

Games, particularly online games, have an ongoing requirement to exhibit the ability to react to player behaviour and change their mechanics and available tools to keep their audience both entertained and feeling that their strategic choices and in-game decisions have value. Game designers invest time both gathering data and analysing it to introduce minor changes that bring their game closer to a state of balance, a task with a lot of potential that has recently come to the attention of researchers. This paper first provides a method for automating the process of finding the best game parameters to reduce the difficulty of Ms PacMan through the use of evolutionary algorithms and then applies the same method to a much more complex and commercially successful PC game, StarCraft, to curb the prowess of a dominant strategy. Results show both significant promise and several avenues for future improvement that may lead to a useful balancing tool for the games industry.

PRESENTATION:
https://doc.co/EFV11d

PAPER:

https://link.springer.com/chapter/10.1007/978-3-319-55849-3_25

Enjoy it!   😀

Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

25 Apr

Amazing paper presented as a talk at EvoGAMES 2017 in Amsterdam (The Netherlands) by Raluca D. Gaina.

Co-authored by: Jialin Liu, Simon M. Lucas, Diego Pérez-Liébana.

ABSTRACT:

Monte Carlo Tree Search techniques have generally dominated General Video Game Playing, but recent research has started looking at Evolutionary Algorithms and their potential at matching Tree Search level of play or even outperforming these methods. Online or Rolling Horizon Evolution is one of the options available to evolve sequences of actions for planning in General Video Game Playing, but no research has been done up to date that explores the capabilities of the vanilla version of this algorithm in multiple games. This study aims to critically analyse the different configurations regarding population size and individual length in a set of 20 games from the General Video Game AI corpus. Distinctions are made between deterministic and stochastic games, and the implications of using superior time budgets are studied. Results show that there is scope for the use of these techniques, which in some configurations outperform Monte Carlo Tree Search, and also suggest that further research in these methods could boost their performance.

PRESENTATION:

https://rdgain.github.io/assets/pdf/EvoStarPdf.pdf

PAPER:

https://link.springer.com/chapter/10.1007/978-3-319-55849-3_28

 

Enjoy it!   😀

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! 😀

Orthogonally Evolved AI to Improve Difficulty Adjustment in Video Games

5 Apr

Paper presented as talk at EvoGAMES 2016 in Porto (Portugal), and selected as one of the best papers of the conference.

BY:
Arend Hintze, Randal Olson, Joel Lehman

ABSTRACT:
Computer games are most engaging when their difficulty is well matched to the player’s ability, thereby providing an experience in which the player is neither overwhelmed nor bored. In games where the player interacts with computer-controlled opponents, the difficulty of the game can be adjusted not only by changing the distribution of opponents or game resources, but also through modifying the skill of the opponents. Applying evolutionary algorithms to evolve the artificial intelligence that controls opponent agents is one established method for adjusting opponent difficulty. Less-evolved agents (i.e. agents subject to fewer generations of evolution) make for easier opponents, while highly-evolved agents are more challenging to overcome. In this publication we test a new approach for difficulty adjustment in games: orthogonally evolved AI, where the player receives support from collaborating agents that are co-evolved with opponent agents (where collaborators and opponents have orthogonal incentives). The advantage is that game difficulty can be adjusted more granularly by manipulating two independent axes: by having more or less adept collaborators, and by having more or less adept opponents. Furthermore, human interaction can modulate (and be informed by) the performance and behavior of collaborating agents. In this way, orthogonally evolved AI both facilitates smoother difficulty adjustment and enables new game experiences.

PRESENTATION:

https://docs.google.com/presentation/d/1AYn6KV7hfQxPIY82wCHVqYS022OQtO9gSABKq3lgX50/pub?start=false&loop=false&delayms=60000#slide=id.p

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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-Sánchez, Juan J. Merelo, Aranzazu San-Ginés, Angel Fernández-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!

Design and Evaluation of an Extended Learning Classifier-based StarCraft Micro AI

4 Apr

Paper presented as poster + interactive presentation at EvoGAMES 2016 in Porto (Portugal).

BY:
Stefan Rudolph, Sebastian von Mammen, Johannes Jungbluth, Jorg Hahner

ABSTRACT:

Due to the manifold challenges that arise when developing an artificial intelligence that can compete with human players, the popular realtime-strategy game StarCraft (BroodWars) has received attention from the computational intelligence research community. It is an ideal testbed for methods for self-adaption at runtime designed to work in complex technical systems. In this work, we utilize the broadly-used Extended Classifier System (XCS) as a basis to develop different models of BW micro AIs: the Defender, the Attacker, the Explorer and the Strategist. We evaluate theses AIs with a focus on their adaptive and co-evolutionary behaviors. To this end, we stage and analyze the outcomes of a tournament among the proposed AIs and we also test them against a non-adaptive player to provide a proper baseline for comparison and learning evolution.
Of the proposed AIs, we found the Explorer to be the best performing design, but, also that the Strategist shows an interesting behavioral evolution.

PRESENTATION:

[poster] http://www.vonmammen.org/broodwars/2016-EvoStar-Broodwars.pdf
[slides] http://www.vonmammen.org/broodwars/XCS-Starcraft-5min-presentation.pdf

Enjoy it!