Multiagent Learning

Directed Reading

I’m planning my directed reading class this coming semester. So, basically I have/get to come up with an entire semester’s worth of material. I might be able to make a class out of it by the time I’m done 🙂 haha. My subjects are focusing on the areas I want to explore with the bounty hunting… Continue reading Directed Reading

Price Surging and Bounty Hunting

So, it seems like uber might have a bounty type pricing model. They have this system of price surging. This we also found doesn’t work for adjusting the bounty. I thought that maybe it would provide a method to get agents to go after the right tasks, it doesn’t. However, it does give me hope that… Continue reading Price Surging and Bounty Hunting

Neural Nets and Agent Hierarchy

So, the idea is that what if we have a hierarchy where some of the agents in the hierarchy have multiple supperiors. Then the underlying gets tasks from both its superiors. How does it learn how much weight to put on each of the tasks. How does it know which to do first? I think… Continue reading Neural Nets and Agent Hierarchy

So, I really want my PhD thesis topic to be on large scale (>8 agents) heterogeneous multi agent learning. The two problems in MAL are speed and generality. The main way I see solving the speed problem is by providing the learning agent information (or background knowledge or reasonable prior probabilities). This has been the… Continue reading Multiagent Learning

MAID

http://dags.stanford.edu/about.html has research in MAS they came up with multi-agent influence diagrams.

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… Continue reading Decision Theory Paradox

Large MAS app. Parking lots

So the idea is that parking lots and local walmarts are not going to be able to afford remaking their parking lots so that when we have autonomous cars we can get dropped off at the door and the robot park somewhere and then pick us up at the entrance. This is because structurally there… Continue reading Large MAS app. Parking lots

Dynamic lane reversal and Braess’ Paradox

http://www.cs.utexas.edu/~pstone/Papers/bib2html-links/ITSC11-hausknecht.pdf Dynamic Lane Reversal in Trafﬁc Management Peter Stone et al. This paper is very interesting. Based on my recent learning about the Price of Anarchy and Braess’ paradox I wonder why their method works. Certainly it is an immediate solution. However, I would imagine that as people learn the system it would degrade and… Continue reading Dynamic lane reversal and Braess’ Paradox

Price of Anarchy

I am using Bounties as a distributed approach to assigning tasks and developing roles for robots in a multi-robot system. I am trying to figure out a good learning algorithm that will get good results for this system that will scale and be general. Therefore, since I’m not that smart I have to learn about… Continue reading Price of Anarchy

Bounties are Prisoners Dillema

For my CS880 project I’m making a bounty system that will hopefully enable robots to learn what roles they should do. I’m currently going to try providing robots a tit-for-tat (or some variation of it) to learn what tasks to do. I think this method might be good since in a real life scenario we… Continue reading Bounties are Prisoners Dillema