Rather than describing the concepts that we happen to have in natural languages, my research seeks to describe the concepts that we ought to have by abstracting on the patterns of natural reasoning. 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 in order to engineer the concepts that are better fit for theory building. In order to extend these traditional methods, I have developed a programmatic methodology for working in semantics in order to draw on the computational power provided by any laptop to rapidly prototype and explore the implications of novel semantic theories (see software for details).

Foundations

Whereas semantics and logic are typically developed for language fragments consisting of the Boolean operators and a small number of novel additions in order to limit the computational complexity required to investigate the resulting theory, this project aims at greater conceptual unity, devising a common semantics and logic for the following operators:

Attempting to develop a unified model theoretic semantics and proof theory for the collection of operators given above is computationally intractable given traditional methods of establishing logical consequences or writing derivations by hand. By contrast, the interactions between the operators given above are readily explored using the programmatic methodology that I have developed in order to support the discovery of adequate semantic theories and the logics. Instead of limiting these tools to the specific languages with which I am concerned, the version of the model-checker currently under development aims to abstract on my semantics, providing a general programmatic methodology for developing and exploring new semantic systems as well as corresponding proof systems. In the next phase of this project, I aim to extend this methodology to include proof assistance so as to streamline the discovery and description of the logics corresponding to each semantic theory.

These methods in conceptual engineering aim to support the development of a unified logical theory with a broad range of applications, including formal verification and AI safety.

Verification

This project provides a flexible, intuitive, and unified semantics and logic for tense, modal, counterfactual conditional, and constitutive explanatory operators in order to model system behavior and verify software. Instead of taking possible worlds (i.e., complete histories of the system) and times to be primitive, the semantics models evolutions as appropriately constrained functions from times to instantaneous world states of the system. In order to interpret hyperintensional operators, world states are identified with maximal possible states where in general states may be partial and impossible. A primitive task relation is then taken to distinguish possible and impossible state transitions, and is subject to a number of constraints and further generalizations. Finite evolutions are then defined to be functions from a finite interval of times (integers) to states where there is a task from the state at each time in the interval to its successor (if any).

In addition to providing a general semantic framework for interpreting a range of logical operators, this project aims to unify the following logics employed in formal methods by providing a common notation in which the following may be expressed as special cases given the expressive power of the present semantics:

Temporal logic (in particular, Leslie Lamport’s TLA+) has emerged as a highly practical tool for modeling programs and systems, with adoption and support within major commercial enterprises like AWS, Microsoft, and Oracle. Other tools used in industry, like Microsoft’s Dafny and the Coq formalization of Iris’s higher-order separation logic, rely on Hoare Logic. Classical varieties of Hoare logic are expressively subsumed by dynamic logic, where both dynamic and temporal logic are at bottom modal systems: the result of enriching a standard extensional deductive logic with modal operators. This project aims to both unify and extend this common lineage, providing an intuitive and general framework in which to gain greater perspective on existing logics used in formal verification as well as providing new affordances given the greater expressive power of the hyperintensional semantics that I have developed.

Interpretability

In order to assist efforts to maintain the alignment of AI-assisted decision-making in socially and morally sensitive sectors, this project develops a semantic methodology to bridge the divide between the opaque parameters of an AI model and the transparent determinations and inferences that may be carried out in human-readable object languages. In addition to applying these resources to better understand the internal mechanisms that explain an AI model’s performance, I am excited about the advances in scientific methodology that the solutions to the interpretability problem in AI may provide. Given models trained on specific datasets— e.g., DNA sequences, weather, etc.— providing methods by which to extract meaningful counterfactuals and causal hypothesis may provide powerful new methodologies with applications across the sciences.

Instead of directly interpreting model parameters or their abstractions, a semantic methodology seeks to construct semantic models (in the sense of model theory) from AI model abstractions in order to evaluate human-readable object languages. I am particularly interested in interpreting languages with counterfactual, causal, relevance, and constitutive explanatory operators in order to explore the complex dependencies and conditional structures within AI models in addition to clarifying responses to edge cases in order to better test for model robustness. Integrating tense and modal operators enables this approach to capture the dynamic aspects of AI decision-making, explaining how outputs evolve over time or respond to initial conditions. Including normative explanatory operators as well as epistemic, indicative conditional, free choice, and deontic modals provides further resources for facing the challenges of surveying and maintain alignment with human values.

The semantic models constructed from AI model abstractions may be thought of as an abstract form of memory. By interpreting human-readable object languages, logical reasoning may be carried out in those languages by appealing to either a semantic theory of logical consequence or else a corresponding proof system. I am especially interested in contributing to designing architectures that allow for dynamic feedback between a AI model and the semantic models over which human-readable object languages are interpreted. This feedback loop resembles human perception which both shapes and is shaped by an agent’s beliefs, allowing for belief revision given sufficient counter-evidence. Just as perceptual modules inform higher cognitive reasoning in human agents without complete transparency— e.g., one does not need to know why a hand appears as a hand to identify it as such— a semantic methodology mediates between the opaque parameters of a AI models and the transparent determinations and patterns of reasoning articulated in a human-readable object language.

Papers

In Progress