Driving in TORCS using modular fuzzy controllers - Poster - EvoGAMES 2017

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Poster for presenting the paper at EvoGAMES 2017 in Amsterdam. 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."

Technology

Experiments and Results
University of Granada (Spain)
What is TORCS?
The Open Racing Car Simulator
Used in Competitions and Testbed for AI
Controllers (Driving bots):
• Sensors: Car and Track sensors
• Actuators: Steer, Accelerator, Break, Gear
Controller Architecture
ANTONIO M. MORA
JUAN JULIÁN MERELO University of Cádiz (Spain)
PABLO GARCÍA-SÁNCHEZ
University of Mascara (Algeria)
MOHAMMED SALEM
salem@univ-mascara.dz
amorag@ugr.es,jmerelo@ugr.es
pablo.garciasanchez@uca.es
STEER Fuzzy Controller
SPEED Fuzzy Controller
IF Front = High THEN TargetSpeed is 280
IF Front = Medium THEN TargetSpeed is 240
IF Front = Low AND M5 = High THEN TargetSpeed is 220
IF Front = Low AND M5 = Medium THEN TargetSpeed is 180
IF Front = Low AND M5 = Low AND M10 = High THEN TargetSpeed is 120
IF Front = Low AND M5 = Low AND M10 = Medium THEN TargetSpeed is 60
IF Front = Low AND M5 = Low AND M10 = Low THEN TargetSpeed is 30
IF Front = MAXDISTSPEED OR M5 = MAXDISTSPEED OR M10 = MAXDISTSPEED
THEN TargetSpeed = 300
IF Front = High THEN steer = 0
IF Front = Medium AND M10 = High THEN steer = 0.25
IF Front = Medium AND M10 = Medium AND M5 = Medium THEN steer = 0.25
IF Front = Medium AND M10 = Medium AND M5 = Low THEN steer = 0.5
IF Front = Low AND M10 = High THEN steer = 0.5
IF Front = Low AND M10 = Medium AND M5 = Medium THEN steer = 0.75
IF Front = Low AND M10 = Medium AND M5 = Low THEN steer = 0.75
Mamdani Fuzzy System
Trapezoidal Membership
AD-SP  Speed Controller
AD-SC  Steering Controller
AD-SSOP  SP + SC [+ Opponent]
SD  Standard Driver
Practise Race
20 laps - No rivals
Time Trial
300 seconds limit
No rivals
Competitive Race
AD-SSOP - Real Competition Teams
Conclusions
Fuzzy controller combining TWO modules instead of just one Good performance in different types of circuits => competitive against real teams
The opponents on race are also considered Too much damage in difficult circuits => the car cannot finish the race

Presentation shown at IEEE Conference on Computational Intelligence and Games 2018, held between 13 and 17 of August in Maastricht (The Netherlands). The ABSTRACT of the work is: Simulated car races have been used for a long time as an environment where car controlling algorithms can be tested; they are an interesting testbed for all kinds of algorithms, including metaheuristics such as evolutionary algorithms. However, the challenge in the evolutionary algorithms is to design a reliable and effective evaluation process for the individuals that eventually translates into good solutions to the car racing problem: finding a controller that is able to win in a wide range of tracks and with a good quantity of opponents. Evaluating individual car controllers involves not only the design of a proper fitness function representing how good the car controller would be in a competitive race, but also the selection of the best solution for the optimization problem being solved; this decision might not be easy when uncertainty is present in the problem environment; in this case, weather and track conditions as well as unpredictable behavior of other drivers. Creating a methodology for the automatic design of the controller of an autonomous driver for a car racing simulator such as TORCS is an optimization problem which offers all these challenges. Thus, in this paper we describe an analysis and some proposals to improve the evaluation of optimized fuzzy drivers for TORCS over previous attempts to do so. It builds on preliminary results obtained in previous papers as a baseline and aims to obtain a more competitive autonomous driver via redesign of the fitness evaluation procedure; to this end, two different fitness functions are studied in several experiments, along with a novel race-based approach for the selection of the best individual in the evolution.

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My slides at Nordic Testing Days 6.6.2024 Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.

Removing Uninteresting Bytes in Software Fuzzing

Aftab Hussain

Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process. In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds. - These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.

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Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs. 1. Unifying Large Language Models and Knowledge Graphs: A Roadmap. https://arxiv.org/abs/2306.08302 2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs: https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/

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Driving in TORCS using modular fuzzy controllers - Poster - EvoGAMES 2017

1. Experiments and Results University of Granada (Spain) What is TORCS? The Open Racing Car Simulator Used in Competitions and Testbed for AI Controllers (Driving bots): • Sensors: Car and Track sensors • Actuators: Steer, Accelerator, Break, Gear Controller Architecture ANTONIO M. MORA JUAN JULIÁN MERELO University of Cádiz (Spain) PABLO GARCÍA-SÁNCHEZ University of Mascara (Algeria) MOHAMMED SALEM salem@univ-mascara.dz amorag@ugr.es,jmerelo@ugr.es pablo.garciasanchez@uca.es STEER Fuzzy Controller SPEED Fuzzy Controller IF Front = High THEN TargetSpeed is 280 IF Front = Medium THEN TargetSpeed is 240 IF Front = Low AND M5 = High THEN TargetSpeed is 220 IF Front = Low AND M5 = Medium THEN TargetSpeed is 180 IF Front = Low AND M5 = Low AND M10 = High THEN TargetSpeed is 120 IF Front = Low AND M5 = Low AND M10 = Medium THEN TargetSpeed is 60 IF Front = Low AND M5 = Low AND M10 = Low THEN TargetSpeed is 30 IF Front = MAXDISTSPEED OR M5 = MAXDISTSPEED OR M10 = MAXDISTSPEED THEN TargetSpeed = 300 IF Front = High THEN steer = 0 IF Front = Medium AND M10 = High THEN steer = 0.25 IF Front = Medium AND M10 = Medium AND M5 = Medium THEN steer = 0.25 IF Front = Medium AND M10 = Medium AND M5 = Low THEN steer = 0.5 IF Front = Low AND M10 = High THEN steer = 0.5 IF Front = Low AND M10 = Medium AND M5 = Medium THEN steer = 0.75 IF Front = Low AND M10 = Medium AND M5 = Low THEN steer = 0.75 Mamdani Fuzzy System Trapezoidal Membership AD-SP  Speed Controller AD-SC  Steering Controller AD-SSOP  SP + SC [+ Opponent] SD  Standard Driver Practise Race 20 laps - No rivals Time Trial 300 seconds limit No rivals Competitive Race AD-SSOP - Real Competition Teams Conclusions Fuzzy controller combining TWO modules instead of just one Good performance in different types of circuits => competitive against real teams The opponents on race are also considered Too much damage in difficult circuits => the car cannot finish the race

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