Bringsjord, Selmer


On Automating the Doctrine of Double Effect

arXiv.org Artificial Intelligence

The doctrine of double effect ($\mathcal{DDE}$) 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 $\mathcal{DDE}$. We briefly present $\mathcal{DDE}$, 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 $\mathcal{DDE}$-compliant autonomous systems from scratch, or one can use it to verify that a given AI system is $\mathcal{DDE}$-compliant, by applying a $\mathcal{DDE}$ 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 $\mathcal{DDE}$ 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 $\mathcal{DDE}$ 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.


The 2015 AAAI Fall Symposium Series Reports

AI Magazine

The Association for the Advancement of Artificial Intelligence presented the 2015 Fall Symposium Series, on Thursday through Saturday, November 12-14, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from four of the symposia.


The 2015 AAAI Fall Symposium Series Reports

AI Magazine

The Association for the Advancement of Artificial Intelligence presented the 2015 Fall Symposium Series, on Thursday through Saturday, November 12-14, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from four of the symposia.


Analogico-Deductive Generation of Gödel's First Incompleteness Theorem from the Liar Paradox

AAAI Conferences

Gödel's proof of his famous first incompleteness theorem (G1) has quite understandably long been a tantalizing target for those wanting to engineer impressively intelligent computational systems. After all, in establishing G1, Gödel didsomething that by any metric must be classified as stunningly intelligent. We observe that it has long been understood that there is some sort of analogical relationship between the Liar Paradox (LP) and G1, and that Gödel himself appreciated and exploited the relationship. Yet the exact nature of the relationship has hitherto not been uncovered, by which we mean that the following question has not been answered: Given a description of LP,and the suspicion that it may somehow be used by a suitably programmed computing machine to find a proof of the incompleteness of Peano Arithmetic, can such a machine, provided this description as input, produce as output a complete and verifiably correct proof of G1? In this paper, we summarize engineering that entails an affirmative answer to this question. Our approach uses what we call analogico-deductive reasoning (ADR), which combines analogical and deductive reasoning to produce a full deductive proof of G1 from LP. Our engineering uses a form of ADR based on our META-R system, and a connection between the Liar Sentence in LP and Gödel's Fixed Point Lemma, from which G1 follows quickly.






Navigating through the Frame Problem: Review of Reasoning Agents in a Dynamic World

AI Magazine

A review of "Reasoning Agents in a Dynamic World: The Frame Problem, Kenneth M. Ford and Pat Hayes, eds., JAI Press, Greenwich, Connecticut, 1991, 289 pp., $68.50, ISBN 1-55938-082-9.