Another reminder: please consider submitting a paper to the CEC 2009 special session on Computational Intelligence and Games, which I am co-organizing together with Pier Luca Lanzi and Daniele Loiacono.
If you're looking to submit your paper to a journal rather than a conference, you might be interested in IEEE Transactions on Computational Intelligence and AI in Games, a new high quality journal that is starting next year (but already accepts submissions) and for which I am an associate editor. Quite a mouthful of a name, but it's bound to be the most important publication outlet for us researchers working in applying CI methods to games.
Thursday, October 16, 2008
CIG 2008 Car racing competition
Just a reminder: please consider submitting to the TORCS-based car racing competition, that I am again organizing together with Daniele Loiacono and Pier Luca Lanzi. The goal is to use your favorite learning algorithm (evolution, td-learning, policy gradients, PSO etc.) and function representation (neural nets, expression trees, rulesets etc.) to develop the best controller for a racing car. Your car needs to win over all the over submitted controllers in a number of races.
Thursday, September 18, 2008
PPSN 2008, post 3
With some hindsight - that is, one day's worth of hindsight - I must say that PPSN 2008 was one of the best conferences I've ever attended. On my very personal ranking, it's up there in the top with CIG 2007 in Hawaii. The organisation of PPSN was top-notch, with nothing going wrong, the food good, the opportunities for socialising/networking plentiful and the schedule adhered to (this is Germany, after all).
It's striking how high the quality of the papers are in PPSN. In Gecco and CEC you know and then find papers you think should not have been accepted in a scientific conference, but never so in PPSN. There is also a difference in "scientificness". Many papers at the two other major conferences present yet another variation on a well-known algorithm, or yet another application, with little in the way of analysis and comparison to the state of the art. At PPSN, the norm seems to be to isolate a particular phenomenon, parameter or operator of known algorithms and benchmarks and study it further, making sure that even papers that are not groundbreaking (which is by necessity most papers) add to the body of human knowledge.
Of course, there are new algorithms and applications at PPSN as well. In particular, a number of variants of the CMA-ES were presented. CMA-ES seems to have become the standard algorithm to benchmark continuous optimization algorithms against, which makes sense, as it reaches good result very quickly on many problems.
Speaking of benchmarks, there seems to be a consensus that there is a lack of good reinforcement learning benchmark. A poster by Marc Schoenauer even went so far as to list "Stop balancing the double pole!" among it's conclusion. Of course, I tried to convince everybody who brought up the topic that they should use simplerace instead. A much better benchmark in many ways.
Now I'm going to prepare the talk I'll give tomorrow (with Marie Gustafsson) on "AI from Science to Fiction" at the Fantastic Film Festival in Lund, and the talk I will give on Monday on "Computational Intelligence and Game Design" at ITU Copenhagen.
It's striking how high the quality of the papers are in PPSN. In Gecco and CEC you know and then find papers you think should not have been accepted in a scientific conference, but never so in PPSN. There is also a difference in "scientificness". Many papers at the two other major conferences present yet another variation on a well-known algorithm, or yet another application, with little in the way of analysis and comparison to the state of the art. At PPSN, the norm seems to be to isolate a particular phenomenon, parameter or operator of known algorithms and benchmarks and study it further, making sure that even papers that are not groundbreaking (which is by necessity most papers) add to the body of human knowledge.
Of course, there are new algorithms and applications at PPSN as well. In particular, a number of variants of the CMA-ES were presented. CMA-ES seems to have become the standard algorithm to benchmark continuous optimization algorithms against, which makes sense, as it reaches good result very quickly on many problems.
Speaking of benchmarks, there seems to be a consensus that there is a lack of good reinforcement learning benchmark. A poster by Marc Schoenauer even went so far as to list "Stop balancing the double pole!" among it's conclusion. Of course, I tried to convince everybody who brought up the topic that they should use simplerace instead. A much better benchmark in many ways.
Now I'm going to prepare the talk I'll give tomorrow (with Marie Gustafsson) on "AI from Science to Fiction" at the Fantastic Film Festival in Lund, and the talk I will give on Monday on "Computational Intelligence and Game Design" at ITU Copenhagen.
Tuesday, September 16, 2008
PPSN 2008, post 2
We've now been to the conference dinner, in an old steel mill. Excellent food, and an excellent place to visit. I love lo-tech and especially huge, rusting metal structures.
Towards the end of the conference dinner, DJ JJ organized a singing competition between Spanish-speakers and a group of speakers of various other tongues(Swedish, English, Dutch, Hebrew etc.). Needless to say, we lost - we couldn't find any songs in English we all knew the lyrics to. After the dinner most of us went back to our respective hotels to recuperate. In my case, I'm recuperating from the excellent Dortmund nightlife. Clubbing until 4.30 on a packed dance floor, with the beers priced 50 cents each. On a Monday. Brilliant!
So you want to know about the scientific side of things? Well, PPSN is really a very good conference. It is hard to find a paper which is not good, though it is of course easy to find papers that don't interest me. I'm not interesting in everything. For example, a theoretical analysis of the behaviour of the 1+1 ES does not interest me very much, even if it is obvious that the research is sound and the paper good. Still, there are many papers here that I like.
Towards the end of the conference dinner, DJ JJ organized a singing competition between Spanish-speakers and a group of speakers of various other tongues(Swedish, English, Dutch, Hebrew etc.). Needless to say, we lost - we couldn't find any songs in English we all knew the lyrics to. After the dinner most of us went back to our respective hotels to recuperate. In my case, I'm recuperating from the excellent Dortmund nightlife. Clubbing until 4.30 on a packed dance floor, with the beers priced 50 cents each. On a Monday. Brilliant!
So you want to know about the scientific side of things? Well, PPSN is really a very good conference. It is hard to find a paper which is not good, though it is of course easy to find papers that don't interest me. I'm not interesting in everything. For example, a theoretical analysis of the behaviour of the 1+1 ES does not interest me very much, even if it is obvious that the research is sound and the paper good. Still, there are many papers here that I like.
Monday, September 15, 2008
PPSN 2008, post 1
I am at PPSN in Dortmund. I am listening to the talk on "Semidefinite Programming and Lift-and-Project Methods in Combinatorial Optimization" by Levent Tunçel. I do not understand anything at all of this talk. I am not the only one in the audience.
While I applaud the PPSN tradition of inviting people who are not evolutionary computation researchers to give keynotes, I don't think people outside our field understand how very little mathematics many (most?) people in this field know.
But the tutorials yesterday and the workshops on Saturday were really nice.
While I applaud the PPSN tradition of inviting people who are not evolutionary computation researchers to give keynotes, I don't think people outside our field understand how very little mathematics many (most?) people in this field know.
But the tutorials yesterday and the workshops on Saturday were really nice.
Saturday, July 26, 2008
What should we call non-evolutionary reinforcement learning?
I work in evolutionary reinforcement learning. That is, I develop reinforcement learning problems, and evolutionary algorithms that solve such problems.
The problem is that many people in reinforcement learning (RL) would say that I'm not working on RL at all. These people work on things like temporal difference learning, policy gradients, or (more likely) some newfangled algorithms I have never heard of, but which are most certainly not evolutionary. Most of the people working on non-evolutionary RL probably don't know much (maybe nothing!) about evolutionary RL either. So disconnected are our communities. It's a shame.
In their discipline-defining (4880 citations on Google Scholar) book "Reinforcement Learning", Sutton and Barto start with defining RL as the study of algorithms that solve RL problems, and mention in passing that they can be solved by evolutionary algorithms as well. The book then mentions nothing more about evolution, and goes on to essentially discuss TD-learning and variations thereof for a few hundred pages.
In practice, the evolutionary and non-evolutionary RL folks publish in different conferences and journals, and don't cite (nor read?) each other much. We write our papers in very different styles (the non-evolutionary RL people having much more maths in them, evolutionary RL researchers often relying on qualitative argument coupled with experimental results), and I for one often simply don't understand non-evolutionary RL papers.
Again, it's a shame. And it would be great if we could find some way of bridging this divide, as we work on the same class of problems.
But to do this, we need to find a way of addressing the issue, which was really the purpose of this blog post. Simply put, what do we call the two classes of algorithms and the research communities studying them? This is an issue I run into now and then, most recently when writing a grant proposal, and now again when preparing lecture slides for a course I'll be teaching this autumn.
The non-evolutionary RL people would not want a negative definition, based on what their algorithms aren't rather than what they are. They would rather go for RL, plain and simple, but this has the problem that non-evolutionary RL is excluded from that field, in spite of being part of the definition. In the proposal we wrote we ended up talking about "classical" versus evolutionary RL, but this has the problem that evolutionary algorithms predated td-learning by several decades. We could also use the term "single-agent RL", but then again, a simple hill-climber is arguably a (degenerate) evolutionary algorithm, and very much single-agent. Besides, there is multi-agent non-evolutionary RL. Sigh.
So I really don't know.
The problem is that many people in reinforcement learning (RL) would say that I'm not working on RL at all. These people work on things like temporal difference learning, policy gradients, or (more likely) some newfangled algorithms I have never heard of, but which are most certainly not evolutionary. Most of the people working on non-evolutionary RL probably don't know much (maybe nothing!) about evolutionary RL either. So disconnected are our communities. It's a shame.
In their discipline-defining (4880 citations on Google Scholar) book "Reinforcement Learning", Sutton and Barto start with defining RL as the study of algorithms that solve RL problems, and mention in passing that they can be solved by evolutionary algorithms as well. The book then mentions nothing more about evolution, and goes on to essentially discuss TD-learning and variations thereof for a few hundred pages.
In practice, the evolutionary and non-evolutionary RL folks publish in different conferences and journals, and don't cite (nor read?) each other much. We write our papers in very different styles (the non-evolutionary RL people having much more maths in them, evolutionary RL researchers often relying on qualitative argument coupled with experimental results), and I for one often simply don't understand non-evolutionary RL papers.
Again, it's a shame. And it would be great if we could find some way of bridging this divide, as we work on the same class of problems.
But to do this, we need to find a way of addressing the issue, which was really the purpose of this blog post. Simply put, what do we call the two classes of algorithms and the research communities studying them? This is an issue I run into now and then, most recently when writing a grant proposal, and now again when preparing lecture slides for a course I'll be teaching this autumn.
The non-evolutionary RL people would not want a negative definition, based on what their algorithms aren't rather than what they are. They would rather go for RL, plain and simple, but this has the problem that non-evolutionary RL is excluded from that field, in spite of being part of the definition. In the proposal we wrote we ended up talking about "classical" versus evolutionary RL, but this has the problem that evolutionary algorithms predated td-learning by several decades. We could also use the term "single-agent RL", but then again, a simple hill-climber is arguably a (degenerate) evolutionary algorithm, and very much single-agent. Besides, there is multi-agent non-evolutionary RL. Sigh.
So I really don't know.
Wednesday, July 02, 2008
CIG 2008 special session on coevolution in games
Coevolution in Games: A Special Session at IEEE Symposium on Computational Intelligence and Games
Perth, Australia
15-18 December, 2008
Special Session Chairs: Julian Togelius, Alan Blair and Philip
Hingston
Contact: julian@togelius.com
Submission deadline: 15 August 2008
http://www.csse.uwa.edu.au/cig08/specialSessions.html
Description:
In coevolution, the fitness of a solution is determined not (only) by a fixed fitness function, but also by the other solution(s) being evaluated. Thus, coevolution has the potential to overcome several problems with static fitness functions, paving the way for more open-ended evolution. However, several phenomena common to coevolutionary algorithms are at present poorly understood, including cycling and loss of gradient. Further understanding of such phenomena would facilitate more widespread use of coevolutionary algorithms.
This special session seeks to bring together research that uses coevolutionary algorithms to learn to play games, uses games to investigate coevolution, or uses coevolution as a basis for game design. Due to their adversarial nature, often involving interaction of multiple agents, games are uniquely suited to be combined with
coevolution. We invite both theoretical and applied work in the intersection of coevolution and games, including but not limited to the following topics:
Competitive coevolution
Cooperative coevolution
Multiple populations in coevolution
Coevolution with diverse representations
Theory of coevolution
Preventing cycling and loss of gradient
Coevolution-based game design
Self-play and coevolutionary-like reinforcement learning
Relative versus absolute fitness metrics
About the organisers:
Julian Togelius is a researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA) in Lugano, Switzerland. His research interests include evolving game-playing agents, modelling player behaviour, and evolving interesting game content, mainly using evolutionary and coevolutionary techniques. He also co-organizes the well-attended Simulated Car Racing Competitions for the IEEE CIG and CEC conferences. See his home page for more information.
Philip Hingston is an associate professor of computer science at Edith Cowan University in Perth. His research interests are in the theory and application of artificial intelligence and computational intelligence. He has a particular interest in evolutionary computation as a tool for design, and in computer games. He is chair of the IEEE CIS Task Force on co-evolution. More information can be found on his home page.
Alan Blair is Chair of the IEEE CIS Task Force on Co-evolution and Games. His research interests include robot navigation, image and language processing as well as co-evolutionary learning for Backgammon, Tron, IPD, simulated hockey and language games. Homepage.
Perth, Australia
15-18 December, 2008
Special Session Chairs: Julian Togelius, Alan Blair and Philip
Hingston
Contact: julian@togelius.com
Submission deadline: 15 August 2008
http://www.csse.uwa.edu.au/cig08/specialSessions.html
Description:
In coevolution, the fitness of a solution is determined not (only) by a fixed fitness function, but also by the other solution(s) being evaluated. Thus, coevolution has the potential to overcome several problems with static fitness functions, paving the way for more open-ended evolution. However, several phenomena common to coevolutionary algorithms are at present poorly understood, including cycling and loss of gradient. Further understanding of such phenomena would facilitate more widespread use of coevolutionary algorithms.
This special session seeks to bring together research that uses coevolutionary algorithms to learn to play games, uses games to investigate coevolution, or uses coevolution as a basis for game design. Due to their adversarial nature, often involving interaction of multiple agents, games are uniquely suited to be combined with
coevolution. We invite both theoretical and applied work in the intersection of coevolution and games, including but not limited to the following topics:
Competitive coevolution
Cooperative coevolution
Multiple populations in coevolution
Coevolution with diverse representations
Theory of coevolution
Preventing cycling and loss of gradient
Coevolution-based game design
Self-play and coevolutionary-like reinforcement learning
Relative versus absolute fitness metrics
About the organisers:
Julian Togelius is a researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA) in Lugano, Switzerland. His research interests include evolving game-playing agents, modelling player behaviour, and evolving interesting game content, mainly using evolutionary and coevolutionary techniques. He also co-organizes the well-attended Simulated Car Racing Competitions for the IEEE CIG and CEC conferences. See his home page for more information.
Philip Hingston is an associate professor of computer science at Edith Cowan University in Perth. His research interests are in the theory and application of artificial intelligence and computational intelligence. He has a particular interest in evolutionary computation as a tool for design, and in computer games. He is chair of the IEEE CIS Task Force on co-evolution. More information can be found on his home page.
Alan Blair is Chair of the IEEE CIS Task Force on Co-evolution and Games. His research interests include robot navigation, image and language processing as well as co-evolutionary learning for Backgammon, Tron, IPD, simulated hockey and language games. Homepage.
Friday, March 14, 2008
Busy days in Hong Kong in June
If you're going to WCCI2008 in Hong Kong in June, you might run into me. Chances are, however, that I'll just keep on running, as I've got a busier schedule than I've ever had in a conference. So just don't take it personally, OK?
To begin with I'm giving a tutorial together with Simon Lucas and Tom Runarsson on "Learning to play games". Preliminarily, Tom will talk about evolution versus other types of reinforcement learning, Simon will talk about different sorts of function approximators, including his n-tuple classifier, and I will discuss different ways in which such techniques can be used in games, with examples. Not only car racing, is the plan...
Then, I will participate in a panel discussion about Computational Intelligence and Games, together with some of the people that are defining this young field.
Staying on the topic of games, I will then present the results of the car racing competition, together with Daniele Loiacono and Pier Luca Lanzi. By the way, have you considered participating? We now have proper software packages available, so it's easy to get started with developing controllers using your favourite brand of learning algorithms.
Yes, I do have some papers as well. However, these are not (primarily) about games this time...
The first paper, a collaboration with my good friends Alberto Moraglio and Renzo De Nardi, is called Geometric PSO + GP = Particle Swarm Programming. The idea is to combine Alberto's geometric particle swarm optimization algorithm with genetic programming. The good thing with GPSO is that it can be used in any search space where a distance between two solutions can be defined, and a weighted recombination operator for these solutions can be devised; in contrast, standard PSO only works in continuous spaces. We have previously successfully used GPSO to solve Sudoku problems, to show this really works. In our new paper, we invent a few weighted recombination operators for expression trees, and test the PSP (GPSO + GP) algorithm on two standard GP benchmarks. The results are decent, but not as good as we would have wanted them; we think this is down to the recombination operators still needing some more work. Nevertheless, we think this is the first time PSO has been applied directly to GP.
The other paper, my first collaboration with Faustino Gomez and Juergen Schmidhuber (my new colleagues at IDSIA) is called Learning what to ignore: Memetic climbing in topology and weight space. It's about "memetic neuroevolution", an idea I've had for a while, and which someone else really should have thought about. The key algorithm here (the "memetic climber") is incredibly simple: evolve topology of weights of a neural network at different time scales. Topology mutations are almost always very destructive, so after each topology mutation, do local search (hill-climbing) in the space of neural network weights do find the potential of the new topology. If the fitness at the end of the local search is not at least as high as it was before the topology mutation, the topology mutation is discarded. This algorithm turns out to work really well, especially for problems where an agent has to handle high-dimensional input arrays. In such problems, which is really the ones i'm most interested in, most neuroevolution algorithms get stuck in local optima if you don't manually specify a good topology for the neural net. So I have big plans for this little algorithm in the future.
To begin with I'm giving a tutorial together with Simon Lucas and Tom Runarsson on "Learning to play games". Preliminarily, Tom will talk about evolution versus other types of reinforcement learning, Simon will talk about different sorts of function approximators, including his n-tuple classifier, and I will discuss different ways in which such techniques can be used in games, with examples. Not only car racing, is the plan...
Then, I will participate in a panel discussion about Computational Intelligence and Games, together with some of the people that are defining this young field.
Staying on the topic of games, I will then present the results of the car racing competition, together with Daniele Loiacono and Pier Luca Lanzi. By the way, have you considered participating? We now have proper software packages available, so it's easy to get started with developing controllers using your favourite brand of learning algorithms.
Yes, I do have some papers as well. However, these are not (primarily) about games this time...
The first paper, a collaboration with my good friends Alberto Moraglio and Renzo De Nardi, is called Geometric PSO + GP = Particle Swarm Programming. The idea is to combine Alberto's geometric particle swarm optimization algorithm with genetic programming. The good thing with GPSO is that it can be used in any search space where a distance between two solutions can be defined, and a weighted recombination operator for these solutions can be devised; in contrast, standard PSO only works in continuous spaces. We have previously successfully used GPSO to solve Sudoku problems, to show this really works. In our new paper, we invent a few weighted recombination operators for expression trees, and test the PSP (GPSO + GP) algorithm on two standard GP benchmarks. The results are decent, but not as good as we would have wanted them; we think this is down to the recombination operators still needing some more work. Nevertheless, we think this is the first time PSO has been applied directly to GP.
The other paper, my first collaboration with Faustino Gomez and Juergen Schmidhuber (my new colleagues at IDSIA) is called Learning what to ignore: Memetic climbing in topology and weight space. It's about "memetic neuroevolution", an idea I've had for a while, and which someone else really should have thought about. The key algorithm here (the "memetic climber") is incredibly simple: evolve topology of weights of a neural network at different time scales. Topology mutations are almost always very destructive, so after each topology mutation, do local search (hill-climbing) in the space of neural network weights do find the potential of the new topology. If the fitness at the end of the local search is not at least as high as it was before the topology mutation, the topology mutation is discarded. This algorithm turns out to work really well, especially for problems where an agent has to handle high-dimensional input arrays. In such problems, which is really the ones i'm most interested in, most neuroevolution algorithms get stuck in local optima if you don't manually specify a good topology for the neural net. So I have big plans for this little algorithm in the future.
Friday, February 22, 2008
Mentioned at the GDC
A paper by me, Renzo and Simon was recently mentioned as "one of the top ten research finding in games studies" at the Game Developers Conference. This is really cool, as GDC is the most important conference for the games industry, attended by all the important developers and industry heads and tons of journalists.
The presentation can be found here:
http://www.avantgame.com/top10.htm
A short description and some discussion on Raph Koster's site:
http://www.raphkoster.com/2008/02/20/gdc2008-game-studies-download-30/
And the paper:
http://julian.togelius.com/Togelius2007Towards
Quite surprising to see that I'm doing "games studies", I thought I was doing artificial intelligence...
The presentation can be found here:
http://www.avantgame.com/top10.htm
A short description and some discussion on Raph Koster's site:
http://www.raphkoster.com/2008/02/20/gdc2008-game-studies-download-30/
And the paper:
http://julian.togelius.com/Togelius2007Towards
Quite surprising to see that I'm doing "games studies", I thought I was doing artificial intelligence...
Thursday, February 21, 2008
Call for participation: IEEE WCCI 2008 Simulated Car Racing Competition
*** SIMULATED CAR RACING COMPETITION @ IEEE WCCI 2008 ***
*** DEVELOPING CONTROLLERS FOR A REALISTIC CAR RACING SIMULATOR ***
Homepage: http://cig.dei.polimi.it/?page_id=5
Deadline: May 25th, 2008
Organizers: Daniele Loiacono, Julian Togelius and Pier Luca Lanzi.
Email: carracing@gmail.com
The car racing competition, organized in association with the 2008 IEEE World Congress on Computational Intelligence WCCI 2008, is now open for submissions. Competition rules and further details can be found on the competition web site:
http://cig.dei.polimi.it/?page_id=5
while recent news can be found at
http://cig.dei.polimi.it/?cat=4
*** GOAL & SCOPE***
The goal of the competition is to develop racing car controllers that can compete on a set of tracks against the timer or other controllers.
There are no limitations on the type of controller, handcoded controllers are also welcome, although the focus of the competition, given the venue, will be on controllers using computational intelligence techniques (e.g. genetic programming, neuroevolution, td-learning, policy gradient search, fuzzy logic, etc.).
*** ORGANIZATION ***
The competition is organized as the successful car racing competitions held in association with CEC2007 and CIG2007, but it is based on "The Open Racing Car Simulator" (TORCS), a realistic car racing simulator, and structured as a client-server architecture.
A racing server will manage the race, while controllers will run as different processes. Communication between the racing server and the controllers is done through TCP/IP. The race is therefore in *real time* and the controller's reaction time is likely to be an important factor in the design.
We will score every submitted controller on the distance raced in a fixed amount of time when driving on its own on a set of tracks. At the end of the competition, the best few controllers will race against each other on a different set of tracks, validating that the controllers perform well in the presence of other cars and that their performance generalizes to other tracks than those they were trained for. The winner of the final competitive races will get to present their controller at WCCI2008 <http://www.wcci2008.org>, and will have their registration fee reimbursed.
*** SOFTWARE ***
The competition software can be downloaded from:
http://cig.dei.polimi.it/?page_id=5
where detailed installation instructions, a description of the sensors model, and examples of controllers are available; more example trainers and interfaces in different programming languages will be added soon.
*** DEVELOPING CONTROLLERS FOR A REALISTIC CAR RACING SIMULATOR ***
Homepage: http://cig.dei.polimi.it/?page_id=5
Deadline: May 25th, 2008
Organizers: Daniele Loiacono, Julian Togelius and Pier Luca Lanzi.
Email: carracing@gmail.com
The car racing competition, organized in association with the 2008 IEEE World Congress on Computational Intelligence WCCI 2008, is now open for submissions. Competition rules and further details can be found on the competition web site:
http://cig.dei.polimi.it/?page_id=5
while recent news can be found at
http://cig.dei.polimi.it/?cat=4
*** GOAL & SCOPE***
The goal of the competition is to develop racing car controllers that can compete on a set of tracks against the timer or other controllers.
There are no limitations on the type of controller, handcoded controllers are also welcome, although the focus of the competition, given the venue, will be on controllers using computational intelligence techniques (e.g. genetic programming, neuroevolution, td-learning, policy gradient search, fuzzy logic, etc.).
*** ORGANIZATION ***
The competition is organized as the successful car racing competitions held in association with CEC2007 and CIG2007, but it is based on "The Open Racing Car Simulator" (TORCS), a realistic car racing simulator, and structured as a client-server architecture.
A racing server will manage the race, while controllers will run as different processes. Communication between the racing server and the controllers is done through TCP/IP. The race is therefore in *real time* and the controller's reaction time is likely to be an important factor in the design.
We will score every submitted controller on the distance raced in a fixed amount of time when driving on its own on a set of tracks. At the end of the competition, the best few controllers will race against each other on a different set of tracks, validating that the controllers perform well in the presence of other cars and that their performance generalizes to other tracks than those they were trained for. The winner of the final competitive races will get to present their controller at WCCI2008 <http://www.wcci2008.org>, and will have their registration fee reimbursed.
*** SOFTWARE ***
The competition software can be downloaded from:
http://cig.dei.polimi.it/?page_id=5
where detailed installation instructions, a description of the sensors model, and examples of controllers are available; more example trainers and interfaces in different programming languages will be added soon.
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