Connectionist Inference Models

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling

Actionable Patient Safety Solution (APSS) #3D: Pediatric Adverse Drug Events

In 2001, the ISMP and the Pediatric Pharmacy Advocacy Group (PPAG) collaborated to produce the nation’s first set of guidelines to reduce pediatric medication errors (ISMP, 2001). The American Academy of Pediatrics (AAP) has also taken a lead in making recommendations to reduce errors (AAP, 2003). Closure of performance gaps and “getting to zero” medication errors will require the constant vigilance from all healthcare professionals and the commitment of hospitals and healthcare systems to implement action in the form of specific leadership, practice and technology plans. This will lead to a decrease in medication errors and a reduction in the occurrence of preventable ADEs in pediatric patients

Stochastic Extended LQR for Optimization-Based Motion Planning Under Uncertainty

We introduce a novel optimization-based motion planner, Stochastic Extended LQR (SELQR), which computes a trajectory and associated linear control policy with the objective of minimizing the expected value of a user-defined cost function. SELQR applies to robotic systems that have stochastic non-linear dynamics with motion uncertainty modeled by Gaussian distributions that can be state- and control-dependent. In each iteration, SELQR uses a combination of forward and backward value iteration to estimate the cost-to-come and the cost-to-go for each state along a trajectory. SELQR then locally optimizes each state along the trajectory at each iteration to minimize the expected total cost, which results in smoothed states that are used for dynamics linearization and cost function quadratization. SELQR progressively improves the approximation of the expected total cost, resulting in higher quality plans. For applications with imperfect sensing, we extend SELQR to plan in the robot's belief space. We show that our iterative approach achieves fast and reliable convergence to high-quality plans in multiple simulated scenarios involving a car-like robot, a quadrotor, and a medical steerable needle performing a liver biopsy procedure

Actionable Patient Safety Solution (APSS) #3D: Pediatric Adverse Drug Events

Preventing ADEs in pediatric patients poses unique challenges because children are particularly vulnerable to adverse outcomes from medication errors (preventable adverse events due to wrong medication use). However, it can create a safety culture, which is a culture that promotes patient safety and quality of care while reducing preventable risks and harm

High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning

As sampling-based motion planners become faster, they can be re-executed more frequently by a robot during task execution to react to uncertainty in robot motion, obstacle motion, sensing noise, and uncertainty in the robot’s kinematic model. We investigate and analyze high-frequency replanning (HFR), where, during each period, fast sampling-based motion planners are executed in parallel as the robot simultaneously executes the first action of the best motion plan from the previous period. We consider discrete-time systems with stochastic nonlinear (but linearizable) dynamics and observation models with noise drawn from zero mean Gaussian distributions. The objective is to maximize the probability of success (i.e., avoid collision with obstacles and reach the goal) or to minimize path length subject to a lower bound on the probability of success. We show that, as parallel computation power increases, HFR offers asymptotic optimality for these objectives during each period for goal-oriented problems. We then demonstrate the effectiveness of HFR for holonomic and nonholonomic robots including car-like vehicles and steerable medical needles