Non-Deterministic Planning With Conditional Effects

Recent advances in fully observable non-deterministic (FOND) planning have enabled new techniques for various applications, such as behaviour composition, among others. One key limitation of modern FOND planners is their lack of native support for conditional effects. In this paper we describe an extension to PRP, the current state of the art in FOND planning, that supports the generation of policies for domains with conditional effects and non-determinism. We present core modifications to the PRP planner for this en-hanced functionality without sacrificing soundness and com-pleteness. Additionally, we demonstrate the planner’s capa-bilities on a variety of benchmarks that include actions with both conditional effects and non-deterministic outcomes. The resulting planner opens the door to models of greater expres-sivity, and does so without affecting PRP’s efficiency.

Computing Contingent Plans via Fully Observable Non-Deterministic Planning

Planning with sensing actions under partial observability is a computationally challenging problem that is fundamental to the realization of AI tasks in areas as diverse as robotics, game playing, and diagnostic problem solving. Recent work on generating plans for partially observable domains has advocated for online planning, claiming that offline plans are often too large to generate. Here we push the envelope on this challenging problem, proposing a technique for generating conditional (aka contingent) plans offline. The key to our planner's success is the reliance on state-of-the-art techniques for fully observable non-deterministic (FOND) planning. In particular, we use an existing compilation for converting a planning problem under partial observability and sensing to a FOND planning problem. With a modified FOND planner in hand, we are able to scale beyond previous techniques for generating conditional plans with solutions that are orders of magnitude smaller than previously possible in some domains

Three Modern Roles for Logic in AI

We consider three modern roles for logic in artificial intelligence, which are based on the theory of tractable Boolean circuits: (1) logic as a basis for computation, (2) logic for learning from a combination of data and knowledge, and (3) logic for reasoning about the behavior of machine learning systems.Comment: To be published in PODS 202

NOX1 loss-of-function genetic variants in patients with inflammatory bowel disease.

Genetic defects that affect intestinal epithelial barrier function can present with very early-onset inflammatory bowel disease (VEOIBD). Using whole-genome sequencing, a novel hemizygous defect in NOX1 encoding NAPDH oxidase 1 was identified in a patient with ulcerative colitis-like VEOIBD. Exome screening of 1,878 pediatric patients identified further seven male inflammatory bowel disease (IBD) patients with rare NOX1 mutations. Loss-of-function was validated in p.N122H and p.T497A, and to a lesser degree in p.Y470H, p.R287Q, p.I67M, p.Q293R as well as the previously described p.P330S, and the common NOX1 SNP p.D360N (rs34688635) variant. The missense mutation p.N122H abrogated reactive oxygen species (ROS) production in cell lines, ex vivo colonic explants, and patient-derived colonic organoid cultures. Within colonic crypts, NOX1 constitutively generates a high level of ROS in the crypt lumen. Analysis of 9,513 controls and 11,140 IBD patients of non-Jewish European ancestry did not reveal an association between p.D360N and IBD. Our data suggest that loss-of-function variants in NOX1 do not cause a Mendelian disorder of high penetrance but are a context-specific modifier. Our results implicate that variants in NOX1 change brush border ROS within colonic crypts at the interface between the epithelium and luminal microbes

On supervising agents in situation-determined ConGolog

We investigate agent supervision, a form of customization, which constrains the actions of an agent so as to enforce certain desired behavioral specifications. This is done in a setting based on the Situation Calculus and a variant of the ConGolog programming language which allows for nondeterminism, but requires the remainder of a program after the execution of an action to be determined by the resulting situation. Such programs can be fully characterized by the set of action sequences that they generate. Hence operations like intersection and difference become natural. The main results of the paper are a characterization of the maximally permissive supervisor that minimally constrains the agent so as to enforce the desired behavioral constraints when some agent actions are uncontrollable, and a sound and complete technique to execute the agent as constrained by such a supervisor. Copyright © 2012, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved