26,744 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
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
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