Game idea and Engineer

App idea would be a game that game you a loop of string (no breaks) and the goal would be to create a given knot.

Also, I was thinking about the definition of engineer.  By its definition it includes some things that I think like artists, which to us aren’t engineers.  I was wondering if this is a problem in our english language that is resolvable in some other language.

Third Year in Phd

http://chronicle.com/article/Your-Third-Year-in-a-PhD/143853/ advice for third year phd.  GMU emailed it to all cs phds.

This semester I’m taking stochastic processes, pattern recognition and CS colloquium.

Neural net follow up

Well a follow up on the neural net idea.  I was thinking that maybe it would be useful as a means of representing a dimension of time.  HyperNEAT’s network takes structure and location into account.  I think that by staggering input nodes, ordering them would be adding the concept of time to how the inputs are weighted.  To get this effect all you need to do is connect an input node to a hidden layer node that has other hidden layer neurons as input.  How would you show that this is true?  I have seen some ANN architectures that have this structure however I have not seen that they argue that by structuring the network in this way that it actually adds any information.

 

Bounty Brokers

Bounty Brokers exist in real life :).  They are the websites that profit from

Like fiverr, the bounty is the service that the users on the site provide.  Of course the bounty hunter is looking to get the most out of its expense of doing the bounty (the analogy to robots is that robots do tasks to get bounties, but the cost to them to achieve the bounty is less than the bounty and in the case of fiverr the cost of me drawing an good illustration is more than paying someone five dollars to do it for me).  So, the broker in the case of fiverr is the website, they take a cut of the bounty of the worker.

Everyone gets paid.  So, Bounty Brokers exist solely to facilitate the interactions of other bondsmen and bounty hunters.

I’m pretty sure in economics they call these systems producers and consumers. 🙂

 

Bounties for Autonomous Traffic and Smart Grid

So, David reminded me of the problem of Coopetition and how that would be an issue with using bounties with the cars.  Also, I love this problem (I’ve looked at it off and on since undergrad). Coopetition is basically the problem is how can competing teams learn to cooperate.  So, here are my thoughts on how I think that a solution may emerge due to how bounties work and who the bondsmen are.

So, on to bounties and the idea of conflicting goals. This is why people shouldn’t be driving :). People don’t compromise because we have no way to really communicate our goals with other drivers other than through “signaling” by essentially going slower or faster and annoying some people. It would be interesting to show in a simulator what the throughput through intersections, time to destination, fuel economy, etc. is when the agents can’t communicate, are greedy and have different objectives. I think that bad things happen. However, it would be good to get a base line of when things start going bad for the scenario.

The main problem I think is that bounties are more locally greedy and that the “world” might want to put a bounty out that helps to cause cooperation among conflicting goals. These could adapt as the “world” observes the agents. The world could be an intersection or a road or something. And the road puts a bounty out for cars to get into some lane or turning left etc.

This could be an entirely different way to work the problem. The cars place their constraints like fuel efficient or fast and their destination and then the world directs them by alerting them to bounties that they may be interested in doing in order to satisfy their constraints in a more cooperative manner. Then this becomes a multiagent planning problem. So, one of the roads could put a bounty out for x number of cars with these properties and then some intersections might want to put out bounties to those cars to change lanes and turn onto that road (that have the properties that the road desired). So, then the car would have to learn whether to do one of the bounties or not and continue on its original route.

I think that this sort of framework would be applicable to smart grid charging and discharging of batteries and consumer devices (like electric cars, air conditions, etc) in order for the grid to be optimal. The cars are the electrons 🙂 wires are the roads, batteries are intersections and houses/appliances are destinations.  Or some variation….

So, essentially I am separating those that are doing the competition (the humans/cars) and those that are cooperating (the road system).  By doing so, the components of the road system are able to use the competitiveness of the agents to manipulate the system.

 

Another idea is that in large networks we may need a broker of bounties.  This would incorporate the work I did with Nil.  The packages in this case would be Bounties….

Games

Would be interesting if console games included smartphone apps that had more game content to augment the game on the console.  Like mini-games to get more gold/money/experience/characters/weapons etc to use in the console game.  Also, by playing the real game different smartphone minigames could be played.

To make money some of the games could offer in app purchases.

Watch Dogs did this but all you did was play against others that were playing it on the pc.

Accident Notification System

So, a friend of mine was just in a really bad car collision (not with another car thankfully).  He was taken to a hospital and was unconscious and had fractured his skull.  It took about 6 hours until his family was notified!  It was on the website of our local newspaper like within the hour of the crash.  I googled it and it seems like others have experienced the same thing where the emergency contacts aren’t called for hours after the collision.

So, my idea is that we could connect a raspberry pi to a bluetooth OBD-II adapter.  Then when the OBD signals that the airbag is deployed the raspberry pi would notify a smartphone to call 911 and any emergency contacts.  However, I did a little googling and found that getting whether the airbag has been deployed would be different for each manufacturer and is proprietary so there is no easy way to get it!  I guess that is why OnStar has got basically a monopoly on providing services like this.

So, I was also thinking that airbags would probably not be the only system affected when in a crash that would cause the airbags to deploy.  Would be interesting to collect data from the OBD to find out what other things are indicators…

So, this idea seems worthy of a kickstarter for Procyon :).  Also, I’ve seen most of the better kickstarters have cool youtube videos.  The guy who was in the accident has a degree in film and has made commercials so maybe we could get him involved.  Would be fun to get into autonomous vehicles eventually.  This might be a good start.

However, back in 2010 someone tried doing this sort of thing only based on the smart phone’s ability to measure Gs by the accelerometer.  Pretty cool.  However, it seems like they had troubles with false positives.  http://mobilware.org/2010/presentations/Car%20Accidents.pdf  But, since then, they haven’t done anything to continue the work.  They did have a neat idea where they would also broadcast location of wreck to a map service so that people could route around it.

EDIT: Well I found someone that offers this as a service http://my-911.com/.  They make you purchase the dongle and then they offer their software as a service that you subscribe to.  They don’t seem to have a big market.  I think that if we could sell it to taxi/limo companies.  Then passengers could provide emergency contact info.  And if the user has the app they would be able to just get into any car and it would be able to interface with the bluetooth device automatically.

EDIT 2: Well I no longer think we should even work on this.  It has been done and done well by https://www.automatic.com/.  It is just about how I would have done it too.  They have a built in accelerometer in the dongle that attaches to the OBD port, so they don’t have to figure out how to know if the air bag has been deployed.  Also, they have really good support so that it isn’t just your phone automatically calling they have a nice protocol set up.  I emailed them and told them about Procyon and an idea I had while I was looking at their site that I think we could provide and a broken link I found on their site.  Cool if they respond.

Decision Theory Paradox

http://en.wikipedia.org/wiki/Ellsberg_paradox is a decision theory paradox.  One solution to the paradox is to use this Choquet integral.  I don’t have time to read about it right now, but it seems like something that would be interesting to observe in multi-agent learning algorithms.  What decision do they learn to take, what would make them learn the Choquet integral method?  Is it stable, meaning do they always chose a particular action in line with a particular method or not?

A paper on the subject: http://ces.univ-paris1.fr/membre/cohen/pdf/gradef.pdf

Seems like it is useful in multi-objective combinatorial optimization: http://eric.univ-lyon2.fr/~arolland/publis/LustRollandMIWAI2013.pdf

Maybe I was asking the wrong questions as no one is looking at it from a MAL perspective.

Automatic feature and target selection

So, I think that I will need to augment how we make features, behaviors and targets in order to get the automatic feature selection (and now automatic target selection as well.  I forgot about how features can take targets!).

We need to annotate them.  Otherwise, I think that we won’t be able to get a good clustering.  Also, annotation will help to pick the features and the corresponding targets.  I think that maybe that induction from the targets for the behaviors to the features might be something to think about.  The annotations are essentially providing the semantics that we as humans take for granted.  Otherwise the clustering would probably not work.

I also think that targets will need to be chosen based on the overall semantics of the learned behavior.  So, like the behavior for determining whether to play on offense or defense was based on a feature distanceTo(me, centerField) I would have to say that this behavior is semantically a location based behavior.  This would then limit the set of behaviors and targets the feature and target selector would have to choose from.  I think that this is enough.  In the future this could be even more generalized.

If this method doesn’t work to pick the targets for the features, then the targets could be chosen in post by the trainer and then the decision tree could be built based off of that.