Tag Archives: Autonomous agents

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! 😀

Advertisements

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 FernĂĄndez Ares, Pablo GarcĂ­a-SĂĄnchez, Antonio M. Mora GarcĂ­a, 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!

Unreal Expert Bots at IWANN 2013

20 Jun

Last week there was held IWANN 2013 at Tenerife, an international conference mainly devoted to researches inside the neural networks scope. In it, Antonio FernĂĄndez Leiva, RaĂșl Lara and Me organized the Special Session on Artificial Intelligence and Games.

There were five works in the session, one of them “Designing and Evolving an Unreal Tournament— 2004 Expert Bot“.

It describes the designing and improvement, through off-line (not during the game) evolution, of an autonomous agent (or bot) for playing the game Unreal Tournament 2004. This was created by means of a finite state machine which models the expert behaviour of a human player in 1 vs 1 deathmatch mode, following the rules of the international competition.

Then, the bot was improved by means of a Genetic Algorithm, yielding an agent that is, in turn a very hard opponent for the medium-level human player and which can (easily) beat the default bots in the game, even in the maximum difficulty level.

The presentation can be seen at:

Moreover, you can watch one example of the evolution in the following video:

Finally, the Unreal Expert and Genetic bot’s source code are available at https://github.com/franaisa/ExpertAgent

Enjoy them. 😉

Super Mario Evolutionary FSM-Based Agent

17 Apr

Recently, inside the last LION 7 (2013) conference (Special Session on Games and Computational Intelligence) there was presented the paper entitled “FSM-Based Agents for Playing Super Mario Game”.

It describes the implementation and test of an autonomous agent which can play Super Mario game better than an expert user can do (in some trained levels).
It is build starting from a Finite State Machine and applying an Evolutionary Algorithm.

The presentation is:

You can watch one example of the obtained agent playing a game here:

Enjoy it. 😉