Logic helps autonomous agents choose best actions amid conflicting rules
A Simple Doxastic Deontic Logic for Norm-Guided Decision Making
Artificial IntelligenceLogic in Computer Science
Summary
Autonomous systems often face tough choices when rules they must follow conflict and they don't have perfect information. The authors propose a simple logical framework that lets an agent consider what it believes about the world and the rules at the same time. Using this logic, an agent can pick the best decision by balancing breaking some rules with how bad it is to break each one. They also find ways to calculate these choices efficiently under certain conditions.
What this means in practice
- •For autonomous system developers: Enable agents to make better decisions when rules conflict and information is uncertain by using belief-aware logic frameworks.
- •For robotic control engineers: Support robots in environments with uncertain facts and competing norms by optimizing decisions to minimize rule violations efficiently.
A theory result. No direct application yet.
Authors
Thorsten Engesser, Agata Ciabattoni
Abstract
Making decisions despite conflicting norms and incomplete or unreliable information is a fundamental challenge for autonomous systems. We introduce a simple doxastic deontic logic for this setting: a classically reducible fragment of Chellas' Minimal Deontic Logic, extended with explicit conditional norms and combined with multi-agent KD45, so that norms can depend on agents' beliefs about both facts and norms. On this logic we define the Doxastic Norm Compliance Optimization Problem, where an agent chooses a decision minimizing weighted norm violations. We distinguish subjective optimization (relative to the agent's beliefs) from objective optimization (relative to the actual facts). We give conditions under which (i) the two coincide and (ii) optimal decision-making can be reduced to weighted partial MaxSAT in polynomial time.