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 extended these traditional methods by developing a computational methodology for rapidly prototyping modular semantic theories with the model-checker. I am also developing a proof-checker package to help implement the proof theory and metalogic for a given semantics in LEAN.

LOGOS: A Formal Language of Thought

Intentional action requires forethought by creating and comparing a range of candidate plans to evaluate the possible risks and consequences of implementing those plans. In addition to representing the current state of affairs along with high-probability trajectories into the open future, 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 project seeks to extend these abilities to AI agents to promote transparent reasoning in an interpreted formal language of thought that can be both audited and verified.

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:

Given a unified semantics for the Logos, logical consequences may be evaluated with the model-checker. I am also working on developing the proof-checker which implements the corresponding proof theory for the Logos in the LEAN proof assistant. These packages provide essential RL feedback for fine-tuning models to reason effectively in the Logos in order to provide scalable oversight for AI reasoning. See software for further details on the packages.

Publications

In Progress