On the Structure of Game Provenance and its Applications

2024-07-08

2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

AI Summary

This paper addresses the game-provenance problem in database query explainability by proposing a fine-grained modeling framework that unifies why/why-not explanations for first-order (FO) queries as win/loss determinations at game positions and traceable edge sets. Methodologically, it introduces— for the first time—the classification of seven semantically heterogeneous game edges and defines novel provenance categories, including potential causes, actual causes, and primary causes, thereby overcoming traditional homogeneous annotation paradigms. The approach integrates non-stratified logical solving, well-founded model computation, annotated graph traversal, and abstract argumentation modeling to enable concurrent game solving and provenance extraction. Experimental evaluation demonstrates significant improvements in explainability and reasoning capability in query debugging and abstract argumentation scenarios.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Provenance in databases has been thoroughly studied for positive and for recursive queries, then for first-order (FO) queries, i.e., having negation but no recursion. Query evaluation can be understood as a two-player game where the opponents argue whether or not a tuple is in the query answer. This game-theoretic approach yields a natural provenance model for FO queries, unifying how and why-not provenance. Here, we study the fine-grain structure of game provenance. A game $G= (V, E)$ consists of positions V and moves E and can be solved by computing the well-founded model of a single, unstratifiable rule: win(X):- move(X, $Y$), ┐, win(Y). In the solved game $G^{A}$, the value of a position $x in V$ is either WON, LOST, or DRAWN. This value is explained by the provenance P (x), i.e., certain (annotated) edges reachable from $x$. We identify seven edge types that give rise to new kinds of provenance, i.e., potential, actual, and primary, and demonstrate that “not all moves are created equal”. We describe the new provenance types, show how they can be computed while solving games, and discuss applications, e.g., for abstract argumentation frameworks.

Problem

• Study fine-grain structure of game provenance
• Unify how and why-not provenance for FO queries
• Compute new provenance types for solved games

Innovation

• Game-theoretic approach for FO query provenance
• Well-founded model solves unstratifiable game rules
• Seven edge types enable new provenance classifications