2,150 research outputs found
On Automating the Doctrine of Double Effect
The doctrine of double effect () is a long-studied ethical
principle that governs when actions that have both positive and negative
effects are to be allowed. The goal in this paper is to automate
. We briefly present , and use a first-order
modal logic, the deontic cognitive event calculus, as our framework to
formalize the doctrine. We present formalizations of increasingly stronger
versions of the principle, including what is known as the doctrine of triple
effect. We then use our framework to simulate successfully scenarios that have
been used to test for the presence of the principle in human subjects. Our
framework can be used in two different modes: One can use it to build
-compliant autonomous systems from scratch, or one can use it to
verify that a given AI system is -compliant, by applying a
layer on an existing system or model. For the latter mode, the
underlying AI system can be built using any architecture (planners, deep neural
networks, bayesian networks, knowledge-representation systems, or a hybrid); as
long as the system exposes a few parameters in its model, such verification is
possible. The role of the layer here is akin to a (dynamic or
static) software verifier that examines existing software modules. Finally, we
end by presenting initial work on how one can apply our layer
to the STRIPS-style planning model, and to a modified POMDP model.This is
preliminary work to illustrate the feasibility of the second mode, and we hope
that our initial sketches can be useful for other researchers in incorporating
DDE in their own frameworks.Comment: 26th International Joint Conference on Artificial Intelligence 2017;
Special Track on AI & Autonom
Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support
A framework and methodology---termed LogiKEy---for the design and engineering
of ethical reasoners, normative theories and deontic logics is presented. The
overall motivation is the development of suitable means for the control and
governance of intelligent autonomous systems. LogiKEy's unifying formal
framework is based on semantical embeddings of deontic logics, logic
combinations and ethico-legal domain theories in expressive classic
higher-order logic (HOL). This meta-logical approach enables the provision of
powerful tool support in LogiKEy: off-the-shelf theorem provers and model
finders for HOL are assisting the LogiKEy designer of ethical intelligent
agents to flexibly experiment with underlying logics and their combinations,
with ethico-legal domain theories, and with concrete examples---all at the same
time. Continuous improvements of these off-the-shelf provers, without further
ado, leverage the reasoning performance in LogiKEy. Case studies, in which the
LogiKEy framework and methodology has been applied and tested, give evidence
that HOL's undecidability often does not hinder efficient experimentation.Comment: 50 pages; 10 figure
Perception is Everything: Repairing the Image of American Drone Warfare
This thesis will trace the United States’ development of unmanned warfare from its initial use in the World Wars through the Cold War to its final maturation in the War on Terror. The examination will provide a summary of unmanned warfare’s history, its gradual adoption, and concerns regarding the proliferation of drones use to understand the emphasis on unmanned weapons in the American Military. In each phase of development, a single program will be focused on to highlight special areas of interest in the modern day. Finally, the modern era of unmanned systems will focus on the growing integration of new weapon systems which no longer fulfill niche roles in the armory but act as fully vetted frontline combatants. Brought together, this examination will show drones have earned their place as integral tools in the American military inventory as faithful defenders of democracy
Toward Formalizing Teleportation of Pedagogical Artificial Agents
Our paradigm for the use of artificial agents to teach requires among other things that they persist through time in their interaction with human students, in such a way that they “teleport” or “migrate” from an embodiment at one time t to a different embodiment at later time t\u27. In this short paper, we report on initial steps toward the formalization of such teleportation, in order to enable an overseeing AI system to establish, mechanically, and verifiably, that the human students in question will likely believe that the very same artificial agent has persisted across such times despite the different embodiments. The system achieves this by demonstrating to the students that different embodiments share one or more privileged beliefs that only one single agent can possess
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