The first step is create the basis for FCA by defining things like objects and attributes within within MeTTa thus also including concepts to be setup. Apart from having attributes that simply define them each object will also have attributes that define how they can interact/blend with other objects. Each of these "relational attributes" are essential to novel concept generation and must be defined explicitly. This milestone covers the definition of a concept in MeTTa as well as the initialization of these concepts and the definition of the "relational attributes" for objects.

The deliverable for this milestone will be the codebase so far. This codebase will include the ability to create a concept in MeTTa. The types of "relational attributes" will be setup for individual objects. Functions for adding new objects or attributes to a concept will be created.

$10,000 USD

A successful implementation of this milestone will be a system that can be able to store created objects and attributes. This system will also allow concepts that link these objects and attributes to be setup. By this milestone the "relational attributes" will also be setup allowing the system to link an object with an attribute signifying its syntactical role in concept creation.

Realtime and adaptive concept learning and generation is possible with FCA algorithms in place that can handle new objects and attributes. These algorithms will have to be both fuzzy and paraconsistent to work with complex real world data. So this milestone covers the development of FCA algorithms for learning about new objects and attributes.

The deliverable for this milestone will be the codebase for the system so far. This system will be able to learn new objects and attributes after being initialized and will be able to integrate these accurately within the existing concept lattice. This means using FCA algorithms to identify the links between existing objects and new attributes as well as new objects and existing attributes.

$15,000 USD

A successful implementation of this milestone will result in a system that can perceive new attributes as well as new objects and be able to fit them within the existing concept lattice. An example of this would be a situation where a system that has been given information about animals and their attributes (size; color; fur etc) sees a new attribute for flight and can identify whether the existing animals possess it or not based on FCA.

Concept generation is the primary focus of this proposal and will be covered in this milestone. This milestone sets up rule-based algorithms to identify compatible concepts across different "mental spaces" for blending them together based on the "relational attributes" previously set up.

The format for the deliverable for this milestone will be the codebase. This codebase now will include rules in MeTTa to identify compatible concepts as well as algorithms to actually blend different input concepts to create novel ideas. The attributes of these novel ideas will be based on the role of each input concept in the final blended concept and will be defined dynamically with additional algorithms.

$15,000 USD

A successful implementation of this milestone would have an agent that can generate novel concepts and ideas realtime. This agent would be able to view different unrelated concepts and blend them based on relational attributes. Eg. Given two concepts Book (which can be about something else) and AI (which is a topic in itself), the agent would be able to combine them for the specific and novel concept (Book about AI).

It is important to setup a fair and reliable evaluation system to ensure that the designed system is functioning as intended. This will be covered through a multi-agent system within in this milestone. This milestone will also include developments of a few demonstrations of the system to showcase and evaluate its capabilities.

The deliverable for this milestone will be the codebase for the multi-agent LLM evaluation framework. It will also include the codebase for the developed demonstrations of this system. This milestone also includes a final report on the results of this evaluation detailing the creativity and novelty of the generated concepts.

$10,000 USD

A successful implementation of this milestone will include well documented demos that can be understood by the rest of the community. It will also include a detailed report about the creativity of the models as well as a maintained codebase for the evaluation framework.

After developing the concept generation system this milestone will implement it within the existing Hyperon system. This implementation will follow the other requirements within the RFP including documentation and an API

The deliverables for this milestone include the codebase being added to Hyperon and complying with the requirements of the RFP. It also includes the detailed documentation for this codebase.

$5,000 USD

The success criteria for this milestone is a successful deployment of the framework on the Hyperon Atomspace and wider codebase. A successful implementation would also include a detailed documentation understood by the wider community such that the framework is highly extensible.

This milestone will include explorations into ideas such as hypervectors to ideate on future improvements. Other such technologies will be researched to identify paths towards more generality within the concept framework.

The deliverable for this milestone will be a report on ideas of exploration beyond the current concept framework that can contribute to the wider AGI approach. The report will also evaluate these ideas depending on their potential.

$5,000 USD

A successful implementation of this milestone will have a well written and detailed technical report that can serve as a basis for future exploration into this component.