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Williams syndrome (WS) is a rare genetic disorder which leads to high social motivation as well as intellectual disability and difficulties with social interaction. Attention to others’ eyes is crucial for social understanding. Individuals with WS are typically highly attentive to faces, but there is a lack of knowledge about how they attend to other’s eyes, and the mechanisms underlying potential alterations in behavior. It has been suggested that physiological hypo-arousal enables individuals with WS to maintain their gaze longer at other’s faces. The aim of this study was to better understand if there is atypical gaze behavior among individuals with WS which might underlie their reduced social understanding and related difficulties with social interaction. We examined the speed and likelihood of gaze shifts to and from other’s eyes in individuals with WS (n = 37; mean age 23 years), and controls (n = 167) in stratified age groups (7 months, 8-12 years, 13-17 years, adults). Peak saccadic velocity was studied as an index of arousal. Individuals with WS were less likely, and slower, to orient to the eyes compared with typically developing controls in all age groups from eight years of age but did not differ from 7-month-old infants. Peak saccadic velocity was reduced in WS, replicating previous results of hypo-arousal. The results were supported by both frequentist and Bayesian statistics. Despite the hyper-social behavioral phenotype, WS is associated with reduced attention to others’ eyes. This could contribute to the difficulties with complex social interaction observed in WS.

To achieve peak performance, it is often necessary to adjust the parameters of a given algorithm to the class of problem instances to be solved; this is known to be the case for popular solvers for a broad range of AI problems, including AI planning, propositional satisfiability (SAT) and answer set programming (ASP). To avoid tedious and often highly sub-optimal manual tuning of such parameters by means of ad-hoc methods, general-purpose algorithm configuration procedures can be used to automatically find performance-optimizing parameter settings. While impressive performance gains are often achieved in this manner, additional, potentially costly parameter importance analysis is required to gain insights into what parameter changes are most responsible for those improvements. Here, we show how the running time cost of ablation analysis, a well-known general-purpose approach for assessing parameter importance, can be reduced substantially by using regression models of algorithm performance constructed from data collected during the configuration process. In our experiments, we demonstrate speed-up factors between 33 and 14 727 for ablation analysis on various configuration scenarios from AI planning, SAT, ASP and mixed integer programming (MIP).

State-of-the-art planners often exhibit substantial runtime variation, making it useful to be able to efficiently predict how long a given planner will take to run on a given instance. In other areas of AI, such needs are met by building so-called empirical performance models (EPMs), statistical models derived from sets of problem instances and performance observations. Historically, such models have been less accurate for predicting the running times of planners. A key hurdle has been a relative weakness in instance features for characterizing instances: mappings from problem instances to real numbers that serve as the starting point for learning an EPM. We propose a new, extensive set of instance features for planning, and investigate its effectiveness across a range of model families. We built EPMs for various prominent planning systems on several thousand benchmark problems from the planning literature and from IPC benchmark sets, and conclude that our models predict runtime much more accurately than the previous state of the art. We also study the relative importance of these features.