2 research outputs found
A Deontic Stit Logic Based on Beliefs and Expected Utility
The formalization of action and obligation using logic languages is a topic
of increasing relevance in the field of ethics for AI. Having an expressive
syntactic and semantic framework to reason about agents' decisions in moral
situations allows for unequivocal representations of components of behavior
that are relevant when assigning blame (or praise) of outcomes to said agents.
Two very important components of behavior in this respect are belief and
belief-based action. In this work we present a logic of doxastic oughts by
extending epistemic deontic stit theory with beliefs. On one hand, the
semantics for formulas involving belief operators is based on probability
measures. On the other, the semantics for doxastic oughts relies on a notion of
optimality, and the underlying choice rule is maximization of expected utility.
We introduce an axiom system for the resulting logic, and we address its
soundness, completeness, and decidability results. These results are
significant in the line of research that intends to use proof systems of
epistemic, doxastic, and deontic logics to help in the testing of ethical
behavior of AI through theorem-proving and model-checking.Comment: In Proceedings TARK 2021, arXiv:2106.1088
Stit Semantics for Epistemic Notions Based on Information Disclosure in Interactive Settings
We characterize four types of agentive knowledge using a stit semantics over
branching discrete-time structures. These are \emph{ex ante} knowledge,
\emph{ex interim} knowledge, \emph{ex post} knowledge, and know-how. The first
three are notions that arose from game-theoretical analyses on the stages of
information disclosure across the decision making process, and the fourth has
gained prominence both in logics of action and in deontic logic as a means to
formalize ability. In recent years, logicians in AI have argued that any
comprehensive study of responsibility attribution and blameworthiness should
include proper treatment of these kinds of knowledge. This paper intends to
clarify previous attempts to formalize them in stit logic and to propose
alternative interpretations that in our opinion are more akin to the study of
responsibility in the stit tradition. The logic we present uses an extension
with knowledge operators of the Xstit language, and formulas are evaluated with
respect to branching discrete-time models. We also present an axiomatic system
for this logic, and address its soundness and completeness