ABSTRACT. Coding agents such as Claude Code, Codex and others are creating a new paradigm that will change programming in ways impossible to foresee. Recent experiences with coding agents indicate that they can easily generate logic programs in Prolog and ErgoAI, as well as lower-level Prolog system code. We conjecture about the implications of these experiences and suggest research pathways to explore the importance of logic programs can in an age of coding agents.

ABSTRACT. We posit that prior and ongoing work on hidden layer neuron analysis gives rise to a neurosymbolic approach to improved cyber-physical systems control via a runtime monitoring system that combines deep neural networks and formal reasoning over knowledge graphs. Such a system would continuously observe neuron activation states (interpreted via neuron labels as system perceptions), assess whether the system perceptions are compatible with actual sensor readings, and suppress system control actions that are deemed as likely mispredictions.

ABSTRACT. Trustworthy AI in the legal domain can be achieved using reasoning-based expert systems. Furthermore, usability and performance of such systems can be improved by using generative AI and adding a human-verification step of generative AI's output-preserving trustworthiness. In addition, the common pitfall of automation bias can be avoided if verification becomes unavoidable and avoidance becomes equivalent to output refutation. We describe a system that demonstrates these claims in practice. By grounding its reasoning in a manually-created Datalog knowledge base with schematic natural-language translations, using an LLM to bridge the gap between natural language case descriptions and Datalog facts, and requiring each generated fact to be human-verified, the system achieves greater usability without compromising on trustworthiness.

ABSTRACT. Abstract: Human-AI Communication for Deontic Reasoning Devops (CODORD) intends to create new, automated techniques for humans to author knowledge about deontics (obligations, permissions, and prohibitions) into an expressively flexible logical language by using natural language (i.e., English). CODORD has the potential to enable automated deontic reasoning with high assurance (i.e., verifiability/explainability and correctness) to assess compliance with orders, regulations, laws, operational policies, and ethics.

ABSTRACT. The Compositional Learning-And-Reasoning for AI Complex Systems Engineering (CLARA) fundamental research program is designed to tightly integrate Automated Reasoning (AR) and Machine Learning (ML) components to create high-assurance AI — which is expected to scale even to complex systems of systems. Integrating the two different branches of AI will provide the speed and flexibility of ML with verifiability based on AR proofs that have strong logical explainability and computational tractability.