Tag Archives: Bot

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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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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Expert Knowledge Modelling in Unreal Tournament presented in CEDI 2013

22 Sep

The title of the paper is “Modelling Human Expert Behaviour in an Unreal Tournament 2004 Bot”. It has presented in the Primer Simposio Español en Entretenimiento Digital (SEED 2013) track, inside CEDI 2013.

Abstract:

This paper presents a deep description of the design of an autonomous agent (bot) for playing 1 vs. 1 dead match mode in the first person shooter Unreal Tournament 2004 (UT2K4).
The bot models most of the behaviour (actions and tricks) of an expert human player in this mode, who has participated in international UT2K4 championships.
The Artificial Intelligence engine is based on two levels of states, and it relies on an auxiliary database for learning about the fighting arena. Thus, it will store weapons and items locations once the player has discovered them, as a human player could do.
This so-called expert bot yields excellent results, beating the game default bots in the hardest difficulty, and even being a very hard opponent for the human players (including the expert).

The presentation can be seen at Slideshare:

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And cite us,of course. 😀