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Relief Camp Manager: A Serious Game Developed Using World Health Organization’s Guidelines

3 May

A formidable paper presented as a talk at EvoGAMES 2017 in Amsterdam (The Netherlands) by Hamna Aslam.

Co-authored by: Anton Sidorov, Nikita Bogomazov, Fedor Berezyuk, Joseph Alexander Brown

ABSTRACT:

Emergency management plans rely on training in order to provide support to first responders, government planners, and affected persons in potential disaster zone. Serious Games have proved to be useful in capturing and invoking people’s attention and emergency management education is also being delivered through games. The paper presents a relief camp game developed using the figures from World Health Organization’s (WHO) report on water, sanitation and hygiene guidelines in emergencies. The game play provides player an understanding of the management of relief camps by giving them a supervisory role to design and organize camp areas. It also encourages players to introduce their own ideas in setting up relief camps. The player is competing against evolutionary computation algorithm. The aims are to create awareness about relief camp management strategies and improving the present approaches for better plans via human competitive testing.

PRESENTATION:

https://www.researchgate.net/project/Relief-Camp-Manager-A-Serious-Game-Developed-Using-World-Health-Organizations-Guidelines

LINK TO THE PAPER:

https://link.springer.com/chapter/10.1007/978-3-319-55849-3_27

https://www.researchgate.net/profile/Joseph_Brown7/publication/313164852_Relief_Camp_Manager_A_Serious_Game_using_the_World_Health_Organization’s_Relief_Camp_Guidelines/links/5891d79d458515aeac941e9f/Relief-Camp-Manager-A-Serious-Game-using-the-World-Health-Organizations-Relief-Camp-Guidelines.pdf

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

Driving in TORCS using modular fuzzy controllers

25 Apr

Astonishing paper presented as interactive presentation and poster at EvoGAMES 2017 in Amsterdam (The Netherlands) by J.J. Merelo.

Co-authored by: Mohammed Salem, Antonio M. Mora, Pablo García-Sánchez.

ABSTRACT:

When driving a car it is essential to take into account all possible factors; even more so when, like in the TORCS simulated race game, the objective is not only to avoid collisions, but also to win the race within a limited budget. In this paper, we present the design of an autonomous driver for racing car in a simulated race. Unlike previous controllers, that only used fuzzy logic approaches for either acceleration or steering, the proposed driver uses simultaneously two fuzzy controllers for steering and computing the target speed of the car at every moment of the race. They use the track border sensors as inputs and besides, for enhanced safety, it has also taken into account the relative position of the other competitors. The proposed fuzzy driver is evaluated in practise and timed races giving good results across a wide variety of racing tracks, mainly those that have many turning points.

POSTER:

 

PRESENTATION:

https://jj.github.io/EVOSTAR_SALEM/#/

PAPER:

https://link.springer.com/chapter/10.1007/978-3-319-55849-3_24

 

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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

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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

 

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Dangerousness Metric for Gene Regulated Car Driving

13 Apr

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

BY:
Sylvain Cussat-Blanc, Jean Disset, Stephane Sanchez

ABSTRACT:

In this paper, we show how a dangerousness metric can be used to modify the input of a gene regulatory network when plugged to a virtual car. In the context of the 2015 Simulated Car Racing Championship organized during GECCO 2015, we have developed a new cartography methodology able to inform the controller of the car about the incoming complexity of the track: turns (slipperiness, angle, etc.) and bumps. We show how this dangerousness metric improves the results of our controller and outperforms other approaches on the tracks used in the competition.

PRESENTATION:

This slideshow could not be started. Try refreshing the page or viewing it in another browser.

https://www.irit.fr/~Sylvain.Cussat-Blanc/shared/slides_evostar16.pdf

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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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