My research develops a unified semantics and logic for the Logos, a formal language of thought to equip AI agents with a capacity to engage in verified reasoning for planning and evaluating actions in coordination with other agents.

Just as the material sciences have devised methods for refining the raw materials of the natural world, philosophical logic employs model theory and proof theory to engineer formal languages that are fit for both theoretical and practical application. I have worked to extend these traditional methods by developing a computational methodology for rapidly prototyping modular semantic theories with the model-checker . I am also implementing the proof theory, semantic theory, and metalogic in LEAN 4 with the proof-checker in order to provide a clean RL signal for training AI systems to reason in the Logos.

LOGOS: A Formal Language of Thought

Responsible human agents engage in planning by reasoning in interpreted languages which include: (1) tense operators for thinking about the past and future along a given course of events; (2) modal operators for reasoning about different courses of events; (3) counterfactual conditional operators for reasoning about minimal changes to a course of events at a given time in order to consider the different futures that may transpire; and (4) causal operators for attributing responsibility. By also reasoning with epistemic and normative operators, human agents are able to cooperate under uncertainty with other agents where the forethought that goes into effective planning remains open to inspection and communication between agents. The Logos seeks to extend these abilities to AI agents to promote verified reasoning in an interpreted formal language that can be both easily audited, providing path towards scalable oversight.

The Logos is designed to train AI systems to conduct verified reasoning in an extensible formal language of thought that is interpreted by explicit semantic models. Including extensional, tense, modal, causal, counterfactual, epistemic, and normative operators in the Logos equips AI systems to perform complex reasoning tasks for planning and evaluating actions in coordination with other agents under conditions of uncertainty. By implementing the axiomatic proof system and recursive semantic theory for the Logos in LEAN 4 and establishes soundness, the proof-checker provides a positive RL signal for training AI systems that is not limited by human annotation or inaccuracy. Implementing the same semantic theory in the model-checker leverages the SMT solver Z3 to find finite countermodels if there are any, providing a negative RL training signal, providing a dual verification architecture.

Whereas logic has traditionally studied small language fragments, the Logos provides a unified semantics and proof system for an extensible range of expressive resources. The operators that have been implemented so far include:

The next phase of this project will extend the Logos to include causal, epistemic, and normative operators for reasoning under uncertainty about imperatives, preferences, and responsibility to facilitate cooperation with human and other AI agents. This includes:

See software for further details.

Publications

In Progress