Tag Archives: EvoGAMES 2016

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:

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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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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:

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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

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There can be only one: Evolving RTS Bots via joust selection

3 Apr

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

BY:
Antonio Fernández Ares, Pablo García-Sánchez, Antonio M. Mora García, Pedro A. Castillo, Juan J. Merelo

ABSTRACT:
This paper proposes an evolutionary algorithm for evolving game bots that eschews an explicit fitness function using instead a match between individuals called joust and implemented as a selection mechanism where only the winner survives. This algorithm has been designed as an optimization approach to generate the behavioural engine of bots for the RTS game Planet Wars using Genetic Programming and has two objectives: first, to deal with the noisy nature of the fitness function and second, to obtain more general bots than those evolved using a specific opponent. In addition, avoiding the explicit evaluation step reduce the number of combats to perform during the evolution and thus, the algorithm time consumption is decreased. Results show that the approach performs converges, is less sensitive to noise than other methods and it yields very competitive bots in the comparison against other bots available in the literature.

PRESENTATION:

 

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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

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Online Evolution for Multi-Action Adversarial Games

3 Apr

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

BY:

Niels Justesen, Tobias Mahlmann, Julian Togelius

ABSTRACT:

We present Online Evolution, a novel method for playing turn-based multi-action adversarial games. Such games, which include most strategy games, have extremely high branching factors due to each turn having multiple actions. In Online Evolution, an evolutionary algorithm is used to evolve the combination of atomic actions that make up a single move, with a state evaluation function used for fitness. We implement Online Evolution for the turn-based multi-action game Hero Academy and compare it with a standard Monte Carlo Tree Search implementation as well as two types of greedy algorithms. Online Evolution is shown to outperform these methods by a large margin. This shows that evolutionary planning on the level of a single move can be very effective for this sort of problems.

PRESENTATION:

 

VIDEO:

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