Our TORCS driving controller presented at EvoGAMES 2017

Last week, @jjmerelo presented at EvoGAMES 2017 (inside Evo* 2017) our work titled “Driving in TORCS using modular fuzzy controllers”.

This paper presents a novel car racing controller for TORCS (The Open Racing Car Simulator), which is based in the combination of two fuzzy subcontrollers, one for setting the speed, and one to control the steer angle. The obtained results are quite promissing, as the controller is quite competitive even against very tough TORCS teams.

The abstract of the paper is:

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.

There was an interactive presentation at the conference, together with a poster:

The paper is available online from: https://link.springer.com/chapter/10.1007/978-3-319-55849-3_24

Enjoy (and cite) it! :D

 

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Unreal Expert Bots at IWANN 2013

Last week there was held IWANN 2013 at Tenerife, an international conference mainly devoted to researches inside the neural networks scope. In it, Antonio Fernández Leiva, Raúl 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 autonomous agents at LION 2013

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. ;)