Anneloes Smitsman

Project Owner

Gamifying Evolutionary Algorithms – Elowyn Game

Expert Rating

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Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Evolutionary algorithms for training transformers and other DNNs

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Overview

This proposal seeks funding to advance the Moloch AI system in the web3 Elowyn: Quest of Time game, in collaboration with SingularityNET. By gamifying evolutionary algorithms, the project trains Transformers and DNNs that can enhance Hyperon’s PRIMUS cognitive architecture. It explores and demonstrates the effectiveness of EMs for: 1) updating node weights of multi-model DNNs to create adaptive, context-aware benevolent AI, and 2) evolving DNN architectures to optimize the Moloch and Elowyn Tree AIs' dynamic challenges, incentivizing a shift from competitive to collaborative play. Elowyn also serves as a cutting-edge AGI development sandbox in support of SingularityNET's goals.

RFP Guidelines

Evolutionary algorithms for training transformers and other DNNs

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $40,000 USD
Proposals 8
Awarded Projects n/a

SingularityNET

Aug. 12, 2024

Explore and demonstrate the use of evolutionary methods (EMs) for training various DNNs including transformer networks. Such exploration could include using EMs to determine model node weights, and/or using EMs to evolve DNN/LLM architectures. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is an example of one very promising evolutionary method among others.

Proposal Description

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Total Milestones
4
Total Budget
$40,000_USD
Last Updated
3 Dec 2024

Milestone 1 - Setting up API Key and AI Server

Description

Develop and set up the API Key integration and server capabilities for connecting the basic behavioral tree AI inside the CCG game-build with the external AI models for the Moloch AI and Elowyn Tree AI.

Deliverables

API Key has been set up and integrated within the game build so that the interface for the in-put and out-put data of the gameplay functions appropriately. This will be done through an open-source implementation of evolutionary DNNs hosted on a public repository with an appropriate OSS license. Time estimate: 1 month.

Budget

$10,000 USD

Success Criterion

API Key integration functions well and AI server has been set up so that training of the EMs for the Moloch and the Elowyn Tree AIs can start. Any model can be connected as long as it follows proper input-output constraints as documented.

Milestone 2 - EM of the Moloch AI

Description

Create a working model of the Moloch AI (DNN) that demonstrates the practical applications of evolutionary AI in gaming. Further we couple the evolution model directly to the objectives of the DNN to generate more a complex system capable of emergent strategic behavior.

Deliverables

Functional code with a tested working model of the Moloch AI (DNN) that showcases the practical application of evolutionary AI in gaming. Ensure that the EM of the Moloch AI can be readily implemented in the Hyperon core architecture. Time estimate: 4 months.

Budget

$20,000 USD

Success Criterion

The EM of the Moloch AI is functioning and can be tested within the Hyperon architecture through an open-source implementation of an evolutionary DNN, hosted on a public repository with an appropriate OSS license.

Milestone 3 - EM of the Elowyn Tree AI

Description

Create a working model of the Elowyn Tree AI (LLMs) that demonstrates the practical applications of evolutionary AI in gaming and how it can guide the evolution of the EM of the Moloch AI.

Deliverables

Functional code with a tested working model of the Elowy Tree AI (LLMs) that showcases the practical applications of evolutionary AI in gaming. Ensure that the EM of the Elowyn Tree AI can be readily implemented in the Hyperon core architecture. Time estimate: 2 months.

Budget

$5,000 USD

Success Criterion

The EM of the Elowyn Tree AI is functioning and can be tested compared to traditional LLM output evaluation and context management through an open-source implementation of evolutionary DNNs, hosted on a public repository with an appropriate OSS license.

Milestone 4 - Evaluation, Testing & Technical Report

Description

Create a comprehensive guide and technical report that explains the experiments so they can be replicated for other AI approaches and include a summary of the evaluation and testing of the EMs that are developed through the project.

Deliverables

1. Provide a comprehensive guide for replicating experiments modifying DNNs and implementing other AI approaches including research results of evaluation and testing Time estimate: 1 month. 2. Provide a Technical Report: Detailed analysis of the evolutionary methods used including strengths weaknesses and comparative results with standard DNNs. Time estimate: 1 month.

Budget

$5,000 USD

Success Criterion

A comprehensive research guide and technical report have been created, with a focus on replicability and integration within the core architecture of Hyperon. In particular, the usefulness of applying the genetic algorithm to a massive DNN, using a multi-model approach.

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The weighted average of the 4 perspectives Overall

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Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

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This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

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Anneloes Smitsman

Project Owner

Dr. Anneloes Smitsman has a doctorate in Systems Science and Sustainability from Maastricht University, the Netherlands. Founder @ CEO of EARTHwise Centre, architect of Elowyn: Quest of Time game.

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AI Engineer

Dr. Rachel St. Clair has a doctorate in Complex Systems and Brain Sciences from Florida Atlantic University. AI engineer and architect for the EARTHwise games project and founder of SImuli.

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Project Director

Project Director as CEO of EARTHwise, architect of Elowyn: Quest of Time game and the evolutionary learning ecosystem for the AI development, training, and gamification of the project.

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