UrbanSim is a simulation environment to experiment with microsimulations if urban development and location choice. It is a tremendous tool to learn and plan about the environment we live in:

It is a valuable tool for improving the level of understanding of how a metropolitan region is developing and how various combinations of land use and transportation policies and investments are likely to shape these trends. Some of the issues of interest, such as affordable housing, are within the scope of the model to be of use, since it deals with predicting housing prices, and disaggregates households by income as well as other characteristics, and can capture the affordability impacts of alternative scenarios. Preservation of land in green space would be feasible to incorporate within the model by earmarking specified parcels for green space preservation, which would influence the supply of land, and could be tested as an attractor for residential or business location. Urban design issues could similarly be explored, given the parcel-level capacity of the developer module, and the ability to incorporate a flexible set of terms in the location choice equations for businesses and households. The specific abilities to test these and other policy issues of interest depend on myriad factors being considered as this planning tool evolves.

P. Waddell, A. Boring, M. Noth, N. Freier, M. Becke, and G. Ulfarsson. Microsimulation of urban development and location choices: Design and implementation of urbansim. Networks and Spatial Economics, 3(1):43–67, 2003.  [url]

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This paper describe the development of the UrbanSim environment, which was developed with the aim of better coordinate the land use and transportation. The software architecture has four principal components: 1. models that encode the behavior of agents in the simulation; 2. a model coordinator, that define the general execution of the models; an object store, that holds the shared representations of agents and other entities in the simulated world; 4. a translation and aggregation layser, that performs a range of data conversions to mediate between the object store and the models.

Of particular interest if the presence of a land price model, which matches between demand and supply of land at different locations.

Real-life reinforcement learning is the topic of building computer systems that learn to improve a measure of performance on a task by interacting with the real world.  It was the topic of a 2004 AAAI Fall Symposium in Washington DC and describes work being

carried out all over the world.

Lab Links:

Autonomous Learning Laboratory: University of Massachusetts

Reinforcement Learning and AI: University of Alberta

Reinforcement Learning at the University of Michigan: University of Michigan

Rutgers Laboratory for Real-Life Reinforcement Learning (RL3): Rutgers University

I know that is kind of old-fashion now, but this kind of projects are into a different light now (in respect of my thesis’ work):

A population of several hundred creatures is created within a supercomputer, and each creature is tested for their ability to perform a given task, such the ability to swim in a simulated water environment. Those that are most successful survive, and their virtual genes containing coded instructions for their growth, are copied, combined, and mutated to make offspring for a new population. The new creatures are again tested, and some may be improvements on their parents. As this cycle of variation and selection continues, creatures with more and more successful behaviors can emerge

.

Breve is a free, open-source software package which makes it easy to build 3D simulations of decentralized systems and artificial life. Users define the behaviors of agents in a 3D world and observe how they interact. breve includes physical simulation and collision detection so you can simulate realistic creatures, and an OpenGL display engine so you can visualize your simulated worlds.

breve is available for Mac OS X, Linux and even Windows!

[url]

L’économie étudie la façon dont les individus ou les sociétés utilisent les ressources rares en vue de satisfaire au mieux leurs besoins. 

J. Généreux, Introduction a l’économie, 2001

Des questions:

1- Qui sont les décideurs (les individus ou les groupes d’individus qui font les choix déterminants?

2- Quels sont les objectifs des décideurs?

3- Quels sont les moyen disponibles et les contraintes?

4- Quelle est la solution optimale?

S. Benford, C. Magerkurth, and P. Ljungstrand. Bridging the physical and digital in pervasive gaming. Communications of the ACM, 48(3):54–57, March 2005. [url]

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This article contains a good review of pervasive games. It also describe the current advantages and limits of technological implementations like: dealing with uncertainty; hybrid architectures (reconciling client-server and peer-to-peer); hefting domains (which elements to represent virtually); multiple locations configurations; orchestration of real-time management and live-games.

This is the official link at the US patent bureau. Sadly, in their background of the invention they forgot to mention that the system was used and exploited ages before (i.e., GeoNotes).

The patent system sometimes makes me want to puke!

I am a bit late on this issue, but I thought it be worth to post my thoughts on the issue. First of all the argument is not new at all: it has been several years that GeoNotes was out and the idea started taking all sorts of forms [1], [2], [3], etc.

Secondly, I am not sure why this became of interests of Phones manufactures more than Service providers. Strange enough because this should be more a service than an hardware implementation.

Thirdly, I don’t understand the cultural differences between the asian market and the european one: why they started putting GPS chips in their phones ages before we can think about it?

Comments are more than welcome!

Geocode, is a service of Tele Atlas to convert form Street name to the Latitude and Longitude information.