Building Trustworthy AI by Addressing its 16+2 Desiderata with Goal-Directed Commonsense Reasoning

The field of AI has seen significant advancements, particularly in deep learning and symbolic reasoning. Deep learning models, such as Large Language Models (LLMs), excel at handling large-scale data but lack explainability and auditability. Symbolic reasoners, like Cyc, provide reasoning chains but are limited by complexity and closed systems. Researchers are exploring neuro-symbolic systems that combine the strengths of both approaches to bridge this gap.

Current AI systems face significant trust issues. While deep learning models are powerful, their decision processes are opaque and difficult to explain or audit. Symbolic reasoners, though providing reasoning chains, struggle with complexity and handling large-scale knowledge bases. Achieving reliable and explainable reasoning while maintaining system simplicity is a pressing challenge.

s(CASP)'s core innovation lies in its goal-directed reasoning and support for constraints. Unlike traditional symbolic reasoners, s(CASP) avoids grounding during execution, maintaining logical variables and constraints. This enables s(CASP) to provide explainable reasoning chains, meeting trustworthy AI requirements. Additionally, s(CASP)'s openness and simplicity make it easier to integrate and extend.

• �� s(CASP) employs goal-directed reasoning, supporting logical variables and constraints.
• �� Uses backward chaining to provide explainable reasoning chains.
• �� Supports constructive negation and coinduction, enhancing reasoning capabilities.
• �� Avoids grounding during execution, maintaining logical variables and constraints.

The experimental design includes testing s(CASP)'s performance across multiple applications, such as task planning and execution in virtual environments and conversational agents. Benchmarks include existing symbolic reasoners and deep learning models, with evaluation metrics focusing on reasoning accuracy, efficiency, and explainability.

s(CASP) excelled in applications like task planning and execution in virtual environments, demonstrating its effectiveness in commonsense reasoning. In the Amazon Alexa Socialbot Grand Challenge, s(CASP)-based chatbots showed excellent conversational abilities. s(CASP) was used in event calculus to model real-world systems and verify timed properties.

s(CASP) has broad applications in fields like law and medicine, such as legal rule reasoning and medical decision support. Its explainability and reliability make it highly impactful in these industries.

s(CASP) may face performance bottlenecks when handling large-scale knowledge bases, requiring further optimization. In some complex reasoning scenarios, additional reasoning mechanisms may be needed. Future research directions include optimizing s(CASP)'s performance, expanding its applications across more domains, and tighter integration with deep learning.