evolveai
Project OwnerPrincipal investigator. Will conduct research into integration methods, develop new algorithms, and test methods.
Assessment of solution quality at a semantic level is limited in current MOSES approach. In order to support the long-term vision of AGI, the proposed effort will enhance MOSES to use LLMs to integrate qualitative assessments of individual solutions and solution sets. Several methods will be explored to augment classic fitness assessment and meta-optimization with search suggestions that will enable MOSES to identify and explore high-value areas in a complex, multi-objective and subjective space.
This RFP invites proposals to explore the integration of LLMs into the MOSES evolutionary algorithm. Researchers can pursue one of several approaches, including generation modeling, fitness function learning, fitness estimation, investigation into domain-independent “cognitively motivated” fitness functions, or propose new innovative ways to leverage LLMs to enhance MOSES's capabilities within the OpenCog Hyperon framework.
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Integrate the LLM to enhance the fitness evaluation function by incorporating domain-specific factors into the evaluation process.
Demonstration of integration
$40,000 USD
Evolutionary process produces solutions that incorporate desired quantitative and qualitative characteristics.
Refine the meta-optimization process by integrating LLM feedback into the evolutionary search. The aim is to ensure that the search process is guided toward more meaningful solutions leveraging domain-specific insights from the LLM.
Recommendations for making targeted use of LLMs in future MOSES applications with strategies for incorporating domain-specific factors and optimizing multi-objective search. Performance benchmarks comparing MOSES’s performance with and without LLM guidance including computational costs and solution quality. Demonstration of best practices for balancing competing objectives using LLM-driven insights with an emphasis on effective integration. Open-source release of functional code for methods developed.
$40,000 USD
Evolutionary process adapts to produce solutions that effectively balance multiple quantitative and qualitative factors. Ideally, solutions are more effective, more appropriate qualitatively, and achieved with low additional computational cost.
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