Cognitive Assemblages

COGNITIVE ASSEMBLAGES Sharpening machine sensibility for architectural design of spatial assemblages through Wave Function Collapse and Reinforcement Learning

Alessandro Mintrone University of Bologna

Introduction Theory overview

Is it possible connect, into the distributed lattice of decisions of the design process, sufficiently complex non-human cognitions capable of claiming part of the authorship, generating spaces, places and everything comprised in the larger spectrum of architecture?

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Manus, robotic installation by Medeline Gannon, at the World Economic Forum.

Cognitive Assemblages

Artificial Neural Networks

Artificial Neural Networks Artificial Intelligence

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AI classifiction scheme, according to Ian Goodfellow in his book Deep Learning Cognitive Assemblages

Artificial Neural Networks Structure

Artificial Neural Networks learn how to map an array of inputs into an array of outputs, passing thru a defined number of internal layers, each one composed by a defined number of units connected to the adjacent layers. What is learned during the training process are the weights of these connections.

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Neural network model with a single hidden layer Cognitive Assemblages

Artificial Neural Networks Example

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Example of a classifier based on a Convolutional Neural Network Cognitive Assemblages

Artificial Neural Networks Latent Space

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Representation of the internal latent space, visualizing categorial boundaries Cognitive Assemblages

Artificial Neural Networks Tools

High level features A neural network can deal with high level features, such as the overral shape of the head of the skin’s texture, capturing non-linear relationships between these features.

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Two images generated within the project: This person does not exist.

Cognitive Assemblages

Wave Function Collapse

Wave Function Collapse Overview

Entropy Analysys

Collapsing Point Selection

Possible Tiles [0, 1, 1, 0, ..., 1, 0]

Contraddiction

Tiles Selection

Diffusion

Algorithmic process

Tileset and resulting assemblage

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Wave Function Collapse Connection rules

Possible Tiles [0, 1, 1, 0, ..., 1, 0]

Contraddiction

Tiles Selection

Single tiles

Permutations

Contraddictions

Assemblage

Possible connections

Forbidden connections

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Wave Function Collapse Information diffusion

Information diffusion Tiles Selection

Diffusion

Set di tiles

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Tile Set 02 Tridimensional oriented

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Tile Set 02 Tridimensional oriented

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Tile Set 03 Tridimensional cubic

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Tile Set 03 Tridimensional cubic

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Tile Set 05 Tridimensional planar

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Tile Set 05 Tridimensional planar

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Reinforcement Learning

Reinforcement Learning Cognition and environment

With Reinforcement Learning the neural network learns from experience. Constructing a feedback-loop by acquiring informatins from the environment, selecting an action and, in this way, transforming the environment itself.

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Feedback loop diagram of the agent interacting with the environment Cognitive Assemblages

Reinforcement Learning Cognition and environment

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AlphaGo, is the first computer program to defeat a professional Go player. The system combines Reinforcement Learning with a Montecarlo tree-search algorithm Cognitive Assemblages

Wave Function Collapse Intelligence

Entropy Analysys

Entropy Analysys

Collapsing Point Selection

Collapsing Point Selection

Possible Tiles

Possible Tiles

[0, 1, 1, 0, ..., 1, 0]

[0, 1, 1, 0, ..., 1, 0]

Contraddiction

Contraddiction

Tiles Selection

Tiles Selection

Diffusion

Neural Network

Diffusion

Algorithmic process

Neural Network inception

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Density

Gradient

Spatial Analysis

Spatial Analysis

1. Density 1 - |desiredDensity - currentDensity|

2. Gradient 1 - fieldValue

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Orientation

Structural Connectivity

Spatial Analysis

Spatial Analysis

3. Orientation allignedTiles / totalTiles

4. Structural Connectivity 1 - tilesUnderThreshold / totalTiles

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Spatial Connectivity

Planar Connectivity

Spatial Analysis

Spatial Analysis

5. Spatial Connectivity 1 - tilesUnderThreshold / totalTiles

6. Planar Connectivity 1 - tilesUnderThreshold / totalTiles

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Machine Learning Rete neurale

3 hidden layers

Input

output

0, 0, 1, . . .

0, 1

0, 0, 1, . . .

0, 1

256 units

256 units

256 units

Neural Network Model

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Machine Learning Analisi e visualizzazione

Parameters visualization on Tensorboard

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Training Heuristic comparison

The Neural Network obtains not only better results in terms of reaching a target density, but also determines non-linear behaviors that led to pattern generation.

Porbabilistic Heuristic Required density: 50% Actual density: 76%

Trained Neural Network Required density: 50% Actual density: 54%

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Training Density

Target value

Analysis Parameters

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Training Orientation

Target value

Analysis Parameters

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Training Connectivity

Target value

Analysis Parameters

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Training Density + Connectivity

Target value

Analysis Parameters

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Training Density + Orientation

Target value

Analysis Parameters

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Training Orientation + Connectivity

Target value

Analysis Parameters

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Training Global

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Discrete Assemblages

Assemblages Fabrication and automation

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Designed by Moshe Safdiefor the 1967 Expo in MontrĂŠal, the Habitat use prefabricated parts and a combinatorial logic for spatial generation

Cognitive Assemblages

Assemblages Fabrication and automation

Ăˆ possibile inserire, nella rete di decisioni del processo progettuale, cognizioni non-umane sufficientemente complesse da assumere in parte la responsabilitĂ della creazione, per generare spazi, luoghi e ogni altro esito che è possibile chiamare architettura?

In questa pagina Installazione di Gilles Retsin per la biennale di architettura di Tallin (TAB) del 2017.

Cognitive Assemblages

Assemblages Parts and connections

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Case study, example of design and connection rules of the selected tileset

Tileset

Connection rules: corners

Connection rules: horizzontal parts

Connection rules: diagonal parts

Connection rules: vertical parts Cognitive Assemblages

Assemblages Comparative Analysis

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Assemblages Comparative Analysis

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Assemblages Comparative Analysis

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Assemblages Analysis overview

Voids analysis

Overview and analysis parameters

Connectivity Analysis

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Assemblages Analysis overview

Plan view

Autonomously generated internal views

Section view

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Assemblages Analysis

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Voids

Connectivity

Plan

Section

Assemblages Analysis

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Voids

Connectivity

Plan

Section

Assemblages Analysis

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Voids

Connectivity

Plan

Section

Assemblages Analysis

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Voids

Connectivity

Plan

Section

Case Study

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Case Study

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Case Study

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Case Study

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Case Study

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Case Study

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Case Study

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Case Study

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COGNITIVE ASSEMBLAGES Alessandro Mintrone University of Bologna alessandro.mintrone@hotmail.com