Driving a Story: Agency and Conflict

If you’re anything like me, you recognise the need for conflict to drive narrative and character development, but at times have difficulty putting two and two together. This can be especially apparent when it comes to tabletop RPG’s where you need to stitch your players’ characters and a narrative together, but how do you create conflict in a character that’s completely outside of your control? What happens in writing where a character needs to do something that we – or our audience – don’t believe they would do? Continue reading

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Adam Koebel’s Office Hours: An Index of Questions Asked

Office Hours is a weekly rollplaying Q&A show hosted by Adam Koebel. Adam is an author/co-designed of the game Dungeon World, and the resident Dungeon Master for both Roll20.net and RollPlay. The complete playlist can be viewed on YouTube here, or via the Twitch VoD’s for subscribers.

Italic entries are questions regarding specific rule sets or “in-jokes”. Continue reading

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AirBnB Preprocessing

Before I was able to start on the application of Dispersive Flies Optimisation to the AirBnB data, it occured to me that while I could use the information as it was, things would be much simpler further down the line if I was to process the data I needed (namely the amenities field) into fields of their own with boolean 0,1 values, than it would be to parse the amenities field each time a fitness check was performed. It should probably be noted early that DFO is intended for search with high dimensionality, My search is likely only going to use a few dimensions and it’s entirely possible that a more conventional search algorithm would perform better.

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GA: Analysis to Implementation

Scenario:

Assume that your GA has chromosomes in the following structure:

ch = (g0, g1, g2, g3, g4, g5, g6, g7)

g0-7 can be any digits between zero to nine.
The fitness of each chromosome is calculated using the following formula:

f(ch) = (g0 + g1) – (g2 + g3) + (g4 + g5 ) – (g6 + g7)
BODMAS is a thing…

This problem is a maximisation problem.

In this scenario we initially have a population of four chromosomes as shown below: Continue reading

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Steady State vs Modular Genetic Algorithms

Once again with definitions and stuff. I’m sure this makes for an absolutely thrilling read. Below we’re talk about the different types of genetic algorithm. Pretty straight forward.

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An Introduction to Genetic Algorithms

Genetic algorithms are a form of evolutionary computation pioneered by one John Henry Holland in 1975. At the time, the main limitation of applying early genetic algorithms was computing power. Because apparently my current computer is like 30,000 times more powerful than my first computer. Yeah… Continue reading

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The Epimia Southlands

It’s been a thousand years since the Great Exodus and little fewer since the Age of Dragons came to an end. The horrors of The Old World have long since been forgotten. Civilisation thrives in the new land of Epimia.

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Fitness Evaluation

This week on Natural Algorithms: We learn some terminology, Kriss makes a wisecrack and a dog does science!

Okay, so the point here is that we’re looking at different types of fitness functions. We divide the type of function an algorithm uses based on what it returns and how it searches for the result. Notably, a continuous or boolean function can be applied to both a full function search and a partial function search. Continue reading

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Algorithm: Stochastic Diffusion Search

Stochastic Diffusion Search is a search algorithm that can take the form of either a neural network or a swarm and attempts an optimal application of resources. The agents scatter randomly across the search area and keep searching random locations until either they or one of their neighbours find a location that they determine to be good. An agent that finds a good location tries to take a neighbour to that location (which will in-turn judge whether it believes the location to be good, or bad, and do the same), while an agent that fails to find a good location will follow a neighbour that has, given the opportunity. Continue reading

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A Potential Application of Dispersive Flies Optimisation

In this post, we’ll be exploring the application of Dispersive Flies Optimisation, as originally pondered in my previous post. Specifically, we’ll discuss applying DFO to AirBnB data, as the AirBnB data is readily available with very little effort. I will be referring to the data provided for London, however all of the available data should be the same.

There are probably loads of ways we can apply DFO to search this information; I’m going to be looking for the best place to stay. Continue reading

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