Tag Archives: Computational Intelligence

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! 😀

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

Enjoy it!

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

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!

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:

 

Enjoy it!

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:

Enjoy it!

EvoGAMES is comming… Look at the CFP

22 Sep

The deadline for submitting your paper to EvoGAMES (and the rest of Evo*) is now set (1 November).

EvoGAMES is a track of the European Conference on the Applications of Evolutionary Computation focused on the applications of bio-inspired algorithms in games.

The areas of interest for the track include, among others:
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

Important dates are:
– Submission of papers: 1 November 2015
– Notification: 4 January 2015
– Camera-ready: 18 January 2015
– Evo* dates: 30 March – 1 April 2016

This year, the page limit has been increased up to 16 pages, so you could write more and more scientific content. 😀

As usual, the accepted submissions will be included in the proceedings of Evo*, published in a volume of the Springer Lecture Notes in Computer Science.

For more info about the conference and the track you can visit the Main site of Evo* 2016.

See you in Porto!