I think that idea is actually a field. Specifically spacial-temporal clustering. There are a lot of highly cited papers in this field. Also, it seems like it sort of tapered off in the mid-2000s. I don’t see any mention of it being applied to a multiagent system other than for cars.
See people in general are getting in each others way when in a crowded environment. When there is a group of people that are working together in this crowd, their removal would cause the system to
The idea is to try and find the least noisy, as the less noisy the
I’m back to wanting to be an agent in a system of unknown but observable agents. I want to learn what the alliances and correlation are between people and agents in the system. I want to be able to take a birds eye view and be able to identify outliers, who is working together or how as an agent could you disguise yourself better so you aren’t detected.
We as humans are able to look at a crowd and we can observe patterns and group dynamics and interesting things, the outliers. We do this very well. A computer on the other hand even when looking at a simulated environment probably wouldn’t do as well as a human in grouping a crowd given a birds eye view. As a human we are able to not only take into account the directional movement, but also profiling and predict then where you expect them to travel. We know what normal looks like, we are aware of it what it looks like in most scenarios.
How can you define that mathematically. What makes observing this mass of agents moving about different from a bunch of particles randomly moving about in the eyes of the observer.
David had an idea for a problem:
Given N individuals, each individual must, say, play a game with M other individuals. The length of the game is stochastic and dependent on who is playing the game. Describe an algorithm that will optimally group the individuals such that each individual has to wait the minimum amount of time to play with all of the other combinations of individuals.
Originally the problem was stated with M=2.
An extension would be what if N is changing with time. Meaning new people come and go. Can the algorithm be robust to this?
So, this seems like it could be approached through a combination of methods:
Stochastic processes (for the arrival and finish times)
Graph Theory / Combinatorics (for the pairing)
Optimization (for the scheduling)
I’m sure that there are other ways, but I think that this problem needs to be solvable pretty quickly in order to be useful…
So, I know that facial recognition is pretty decent, but I think that HyperNEAT would be pretty good at doing this. I think that it would be able to take advantage of the geometry of the face in order to better characterize the face. I have found a paper that used HyperNEAT on digits, and it did not do extremely well. They found that HyperNEAT is good at extracting useful features that could then be used to classify. So, maybe that would be something else to look at.
http://www.aaai.org/Library/AAAI/aaai-library.php took me forever to find this and this http://www.aaai.org/ocs/index.php/AAAI/AAAI14/search. Very valuable.
Then we have AAMAS proceedings: http://dl.acm.org/event.cfm?id=RE146&tab=pubs&CFID=454223637&CFTOKEN=11498247.
I found my idea that I had for the adaptive auctions. This isn’t the full general case of negotiation, but it is a start. http://www.sigecom.org/exchanges/volume_5/5.3-Pardoe.pdf And its by Peter Stone :).
So, 2 ideas 1. use that ultrasound based display to act as a braille display, so blind people could use it as a way to read and interact with the device.
Then the other idea is to have a smartphone app that converts pictures of braille into english alphabet words… I think computer vision would be able to handle that.
This is cool. He discusses the concept of contracting and subcontracting for building houses and puts it into the perspective of sattelites and task allocation.
So, what if we think of bounties as an ascending auction? The idea is that we have a bunch of tasks that we are auctioning off. Multiple agents can bid on a task (going after the task) at the same time. They win the auction by successfully completing the task. Like in bounties the price increases as time goes until a winner is determined.
The main difference is that in an auction the bids are “virtual” and only the winner must pay the price. Whereas with bounties the bids are real and the losers pay the amount they spent trying going after that task.
The interesting thing about thinking about bounties this way is to see that an ascending auction would imply the ability to “jump ship”, or bidding on different things without finishing, which we found, at least for bounties, to be detrimental to the system.
In branching processes you can find the probability of the system going extinct by finding the roots of the polynomial where the coefficients are the probabilities and the exponents are the number generated. The smallest root between 0 and 1 will be the answer. What do the other roots mean? Basically the root corresponds to when the system is zero (extinct). We can have all kinds of roots: complex, negative, positive > 1 roots. So, what do they mean? Do they mean anything. Well wikipedia has a page on exotic probability so, I guess they might have some meaning… http://en.wikipedia.org/wiki/Exotic_probability. Can’t find anything on the subject so who knows…