activity
20242026
most citedA Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

math.MG2026

In Search of Melchior's Ordinary Points

Jonathan Lenchner, Rik Sengupta

In 1893, James Joseph Sylvester posed the following problem: given n points in the plane, not all collinear, must there be a line determined by two of the points that does not pass…

cs.LO2025

Who Wins the Multi-Structural Game?

Ronald Fagin, Neil Immerman, Phokion Kolaitis +2

Combinatorial games played between two players, called Spoiler and Duplicator, have often been used to capture syntactic properties of formal logical languages. For instance, the w…

cs.IT2024

Breaking through the classical Shannon entropy limit: A new frontier through logical semantics

Luis A. Lastras, Barry M. Trager, Jonathan Lenchner +4

Information theory has provided foundations for the theories of several application areas critical for modern society, including communications, computer storage, and AI. A key asp…

cs.LO2024

On the Number of Quantifiers Needed to Define Boolean Functions

Marco Carmosino, Ronald Fagin, Neil Immerman +3

The number of quantifiers needed to express first-order (FO) properties is captured by two-player combinatorial games called multi-structural games. We analyze these games on binar…

cs.AI2024★ 1 cited

A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making

Chitra Subramanian, Miao Liu, Naweed Khan +5

Multi-agent reinforcement learning (MARL) is well-suited for runtime decision-making in optimizing the performance of systems where multiple agents coexist and compete for shared r…

cs.LO2024

Parallel Play Saves Quantifiers

Marco Carmosino, Ronald Fagin, Neil Immerman +4

The number of quantifiers needed to express first-order properties is captured by two-player combinatorial games called multi-structural (MS) games. We play these games on linear o…