Objective: Design hybrid DNN architectures that integrate symbolic reasoning with neurocognitive methods. Research Goals: Develop hybrid architectures combining symbolic logic and deep learning. Test models on real-world tasks such as semantic understanding and decision-making. Improve interpretability without compromising performance. Student Roles: Architect Developer: Designs hybrid DNN frameworks. Implementation Specialist: Implements models in Python or similar environments. Evaluation Analyst: Conducts experiments on real-world datasets. Literature Researcher: Reviews related works and best practices. Communications Lead: Prepares research papers and presentations.
This RFP invites proposals to explore and demonstrate the use of neuro-symbolic deep neural networks (DNNs), such as PyNeuraLogic and Kolmogorov Arnold Networks (KANs), for experiential learning and/or higher-order reasoning. The goal is to investigate how these architectures can embed logic rules derived from experiential systems like AIRIS or user-supplied higher-order logic, and apply them to improve reasoning in graph neural networks (GNNs), LLMs, or other DNNs.
Hybrid Architecture Design
Deliverable: Blueprint of proposed neuro-symbolic architecture models.
$30,000 USD
Proposed architecture combines symbolic and neural approaches to exceed baseline performance in defined test cases.
Prototype Implementation
Deliverable: Functional prototypes for at least two hybrid architectures.
$20,000 USD
Early implementations achieve at least 10% improvement in interpretability and predictive accuracy.
Validation and Performance Analysis
Deliverable: Report on model accuracy, efficiency, and scalability tests.
$12,000 USD
Models validated against benchmark datasets show performance gains in efficiency and reduced computational costs.
Model Refinement
Deliverable: Updated models with improved performance metrics.
$10,000 USD
Developed architectures demonstrate scalability and adaptability for real-world industrial applications.
Final Publications and Knowledge Dissemination
Deliverable: Published papers and presentation materials for academic and industry conferences.
$8,000 USD
Results published in reputable journals, with presentation at major conferences on AI or neural networks.
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