PUTTING THE HUMAN BACK INTO THE LOOP: THE HOWS PROJECT

THE HOWS PROJECTProject started February 2017EPSRC-funded 3 year project

Prof Dragan Savic Prof Edward Keedwell

Dr David Walker Dr Matt Johns

PROJECT PARTNERS

PROJECT TEAM

PROJECT AIM“To develop a new approach for designing and managing improved, near-optimal and engineering-intuitive water systemsby incorporating visual analytics, heuristic optimisation and feedback-informed learning.”

PUTTING THE HUMAN BACK IN THE LOOPAutomated optimisation methods tend to remove the expert from the optimisation process

Highly optimal solutions can be generated in terms of their simulated performance, but fall short for practical implementation in the real world

Research suggests expert knowledge and intervention is essential to produce valid solutions for real-world problems

UNDERSTANDING THE CURRENT STATE-OF-THE-ART Extensive review of water systems design and operations software used in industry

Visualisation techniques Hydraulic modelling capabilities

Integrated optimisation tools Which platforms are being used

INTERACTIVE 3D VISUALISATION

INTERACTIVE OPTIMISATION PLATFORM

Intuitive 3D water distribution network representation Visualisation of optimisation

What’s in the box?

Intuitive 3D representation

Elevations and pipe sizes visualised simultaneously & intuitively

New innovative techniques for the visualisation of hydraulic and optimisation parameters

3D NETWORK VISUALISATION

INTERACTIVE OPTIMISATION PLATFORM

An attempt to “lift the lid” on the optimization algorithm “black box”

Little work has been conducted into the visualisation of networks that have been subjected to optimisation

Information on the interaction between optimiser and network can yield important insights into network and optimiser performance

The additional visualisation capability of the 3D representation can be used to show information from the optimisation

VISUALISATION OF OPTIMISATION

INTERACTIVE OPTIMISATION PLATFORM

INTERACTIVE OPTIMISATION PLATFORMOptimisation & Machine Learning

SYSTEM OVERVIEWServer - Optimisation and machine learning Flexible Scalable

Client - Interactive visualisation Desktop

Virtual Reality Web Mobile

OPTIMISATION

Use an evolutionary algorithm to solve a computationally expensiveoptimisation problem by generating solutions that an expert might not think about

OPTIMISATION EXAMPLE: DESIGNING CHAIRS

Generate a population of potential solutions to a given problem – a set of random chair designs

EVALUATING SOLUTION QUALITY

y = f(x)Evaluate solution quality with an objective function – a mathematical function which may depend on a model of the real world

SELECT SOME OF THE GOOD SOLUTIONS

COMBINE SOME OF THE SELECTED SOLUTIONS’ ATTRIBUTES

CROSSOVER

CHANGE SOME OF THE NEW SOLUTIONS’ ATTRIBUTES

MUTATION

MUTATION

EVALUATE THE SOLUTIONS AND UPDATE THE POPULATION

WATER DISTRIBUTION NETWORK DESIGN

Optimise the pipes used in a WDN

Mutation heuristics modify pipe diameters

Evaluate solution quality with EPANET

PUTTING THE HUMAN IN THE LOOP: INTERACTIVE OPTIMISATIONOptimisation produces highly optimal solutions that can be impractical for implementation in the real world

Ask the expert to• Select the solutions they think are best suited to solving the problem• Generate new solutions by mutating existing solutions themselves

Problems with interaction

User fatigue is a problem with interactive optimisation – users become bored with a repetitive task and make choices at random

Use machine learning to supplement the user interactions by understanding how they use the system

UNDERSTANDING ATTITUDES TO OPTIMISATION

Evolutionary optimisation is an active area of research within academia but its uptake is often slow in the water industry

A survey will enable us to understand industry attitudes to optimisation to design a better interactive system

Find the survey online at

http://howsproject.uk

QUESTIONS?

www.howsproject.uk @HOWSProject