| id |
ddss9411 |
| authors |
Bouillé, Francois |
| year |
1994 |
| title |
Mastering Urban Network Intersection And Superimposition, in an Object-oriented Knowledge System Integrating Rules, Neurons and Processes |
| source |
Second Design and Decision Support Systems in Architecture & Urban Planning (Vaals, the Netherlands), August 15-19, 1994 |
| summary |
Many networks cover the urban texture, either superimposed at a variable distance, or really intersecting, or even in interconnection. We briefly recall the HBDS model, working on persistent abstract data types associated to graphical representations and carrying algorithms expressing conditions to be verified and/or actions to be performed. HBDS is an integrated system too, including database, expert system dealing with fuzzy rules and facts, discrete simulation engine, and neural engine; it has a general purpose programming language. Any urban network is associated to a given prototype, according to the same scheme named prototype with more specific components. These prototypes allow to build the different thematic structures instantiations of the prototypes. All possible cases of arc intersection or "pseudo-intersection" (simple superimposition)or interconnection are obtained by, owing to new prototypes. Moreover, such (pseudo)-intersections are automatically recognized and processed without a human intervention, owing to classes ofconstraints and classes of rules. They deal with particular constraints concerning the location of some urban furniture, and rules concerning the way a cable or a pipe must follow according to thepre-existing other networks in a given area, the minimal distances, minimal or maximal depths, and some required equipments. Urban classes of (pseudo-)intersections inserted in the hyperciass"neuron", inheriting of neural features, may be used for automated learning of urban knowledge; owing to their "behavior", these neurons can communicate and perform actions on other components. Urban classes inserted in the hyperciass "process" may be used for building very large models simulating complex urban phenomenons, thus allowing a better understanding of the real phenomenons. As a conclusion, we emphasize the methodological aspects of object-oriented integration for an efficient processing of the urban context, based on prototyping and mixing rules, neurons and processes. |
| series |
DDSS |
| references |
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| last changed |
2003/08/07 16:36 |
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