Don't bet on luck alone: enhancing behavioral reproducibility of quality-diversity solutions in uncertain domains

Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space. However, in the presence of unpredictable noise, the fitness and descriptor of the same solution can differ significantly from one evaluation to another, leading to uncertainty in the estimation of such values. Given the elitist nature of QD algorithms, they commonly end up with many degenerate solutions in such noisy settings. In this work, we introduce Archive Reproducibility Improvement Algorithm (ARIA); a plug-and-play approach that improves the reproducibility of the solutions present in an archive. We propose it as a separate optimization module, relying on natural evolution strategies, that can be executed on top of any QD algorithm. Our module mutates solutions to (1) optimize their probability of belonging to their niche, and (2) maximize their fitness. The performance of our method is evaluated on various tasks, including a classical optimization problem and two high-dimensional control tasks in simulated robotic environments. We show that our algorithm enhances the quality and descriptor space coverage of any given archive by at least 50%

Towards Anchoring Self-Learned Representations to Those of Other Agents

In the future, robots will support humans in their every day activities. One particular challenge that robots will face is understanding and reasoning about the actions of other agents in order to cooperate effectively with humans. We propose to tackle this using a developmental framework, where the robot incrementally acquires knowledge, and in particular 1) self-learns a mapping between motor commands and sensory consequences, 2) rapidly acquires primitives and complex actions by verbal descriptions and instructions from a human partner, 3) discovers correspondences between the robots body and other articulated objects and agents, and 4) employs these correspondences to transfer the knowledge acquired from the robots point of view to the viewpoint of the other agent. We show that our approach requires very little a-priori knowledge to achieve imitation learning, to find correspondent body parts of humans, and allows taking the perspective of another agent. This represents a step towards the emergence of a mirror neuron like system based on self-learned representations

QuerySnout: automating the discovery of attribute inference attacks against query-based systems

Although query-based systems (QBS) have become one of the main solutions to share data anonymously, building QBSes that robustly protect the privacy of individuals contributing to the dataset is a hard problem. Theoretical solutions relying on differential privacy guarantees are difficult to implement correctly with reasonable accuracy, while ad-hoc solutions might contain unknown vulnerabilities. Evaluating the privacy provided by QBSes must thus be done by evaluating the accuracy of a wide range of privacy attacks. However, existing attacks against QBSes require time and expertise to develop, need to be manually tailored to the specific systems attacked, and are limited in scope. In this paper, we develop QuerySnout, the first method to automatically discover vulnerabilities in query-based systems. QuerySnout takes as input a target record and the QBS as a black box, analyzes its behavior on one or more datasets, and outputs a multiset of queries together with a rule to combine answers to them in order to reveal the sensitive attribute of the target record. QuerySnout uses evolutionary search techniques based on a novel mutation operator to find a multiset of queries susceptible to lead to an attack, and a machine learning classifier to infer the sensitive attribute from answers to the queries selected. We showcase the versatility of QuerySnout by applying it to two attack scenarios (assuming access to either the private dataset or to a different dataset from the same distribution), three real-world datasets, and a variety of protection mechanisms. We show the attacks found by QuerySnout to consistently equate or outperform, sometimes by a large margin, the best attacks from the literature. We finally show how QuerySnout can be extended to QBSes that require a budget, and apply QuerySnout to a simple QBS based on the Laplace mechanism. Taken together, our results show how powerful and accurate attacks against QBSes can already be found by an automated system, allowing for highly complex QBSes to be automatically tested "at the pressing of a button". We believe this line of research to be crucial to improve the robustness of systems providing privacy-preserving access to personal data in theory and in practice

Возможности радикального лечения варикотромбофлебита на фоне варикозной болезни и трофических язв

КОНЕЧНОСТИ НИЖНЕЙ ЯЗВАТРОМБОФЛЕБИТ /ХИРВАРИКОЗНАЯ ЯЗВАВАРИКОЗНОЕ РАСШИРЕНИЕ ВЕН /ОС

Empirical analysis of PGA-MAP-Elites for neuroevolution in uncertain domains

Quality-Diversity algorithms, among which MAP-Elites, have emerged as powerful alternatives to performance-only optimisation approaches as they enable generating collections of diverse and high-performing solutions to an optimisation problem. However, they are often limited to low-dimensional search spaces and deterministic environments. The recently introduced Policy Gradient Assisted MAP-Elites (PGA-MAP-Elites) algorithm overcomes this limitation by pairing the traditional Genetic operator of MAP-Elites with a gradient-based operator inspired by Deep Reinforcement Learning. This new operator guides mutations toward high-performing solutions using policy-gradients. In this work, we propose an in-depth study of PGA-MAP-Elites. We demonstrate the benefits of policy-gradients on the performance of the algorithm and the reproducibility of the generated solutions when considering uncertain domains. We first prove that PGA-MAP-Elites is highly performant in both deterministic and uncertain high-dimensional environments, decorrelating the two challenges it tackles. Secondly, we show that in addition to outperforming all the considered baselines, the collections of solutions generated by PGA-MAP-Elites are highly reproducible in uncertain environments, approaching the reproducibility of solutions found by Quality-Diversity approaches built specifically for uncertain applications. Finally, we propose an ablation and in-depth analysis of the dynamic of the policy-gradients-based variation. We demonstrate that the policy-gradient variation operator is determinant to guarantee the performance of PGA-MAP-Elites but is only essential during the early stage of the process, where it finds high-performing regions of the search space

Does wage rank affect employees' well-being?

How do workers make wage comparisons? Both an experimental study and an analysis of 16,000 British employees are reported. Satisfaction and well-being levels are shown to depend on more than simple relative pay. They depend upon the ordinal rank of an individual's wage within a comparison group. “Rank” itself thus seems to matter to human beings. Moreover, consistent with psychological theory, quits in a workplace are correlated with pay distribution skewness

Barriers to colorectal cancer screening among American Indian men aged 50 or older, Kansas and Missouri, 2006-2008

American Indian (AI) men have some of the highest rates of colorectal cancer (CRC) in the United States but among the lowest screening rates. Our goal was to better understand awareness and discourse about colorectal cancer in a heterogeneous group of AI men in the Midwestern United States. Focus groups were conducted with AI men (N = 29); data were analyzed using a community-participatory approach to qualitative text analysis. Several themes were identified regarding knowledge, knowledge sources, and barriers to and facilitators of screening. Men in the study felt that awareness about colorectal cancer was low, and people were interested in learning more. Education strategies need to be culturally relevant and specific

Evolving Robots on Easy Mode: Towards a Variable Complexity Controller for Quadrupeds

The complexity of a legged robot's environment or task can inform how specialised its gait must be to ensure success. Evolving specialised robotic gaits demands many evaluations - acceptable for computer simulations, but not for physical robots. For some tasks, a more general gait, with lower optimization costs, could be satisfactory. In this paper, we introduce a new type of gait controller where complexity can be set by a single parameter, using a dynamic genotype-phenotype mapping. Low controller complexity leads to conservative gaits, while higher complexity allows more sophistication and high performance for demanding tasks, at the cost of optimization effort. We investigate the new controller on a virtual robot in simulations and do preliminary testing on a real-world robot. We show that having variable complexity allows us to adapt to different optimization budgets. With a high evaluation budget in simulation, a complex controller performs best. Moreover, real-world evolution with a limited evaluation budget indicates that a lower gait complexity is preferable for a relatively simple environment.Comment: Accepted to EvoApplications1

Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm

During the last two decades, various models have been proposed for fish collective motion. These models are mainly developed to decipher the biological mechanisms of social interaction between animals. They consider very simple homogeneous unbounded environments and it is not clear that they can simulate accurately the collective trajectories. Moreover when the models are more accurate, the question of their scalability to either larger groups or more elaborate environments remains open. This study deals with learning how to simulate realistic collective motion of collective of zebrafish, using real-world tracking data. The objective is to devise an agent-based model that can be implemented on an artificial robotic fish that can blend into a collective of real fish. We present a novel approach that uses Quality Diversity algorithms, a class of algorithms that emphasise exploration over pure optimisation. In particular, we use CVT-MAP-Elites, a variant of the state-of-the-art MAP-Elites algorithm for high dimensional search space. Results show that Quality Diversity algorithms not only outperform classic evolutionary reinforcement learning methods at the macroscopic level (i.e. group behaviour), but are also able to generate more realistic biomimetic behaviours at the microscopic level (i.e. individual behaviour).Comment: 8 pages, 4 figures, 1 tabl

Employing external facilitation to implement cognitive behavioral therapy in VA clinics: a pilot study

<p>Abstract</p> <p>Background</p> <p>Although for more than a decade healthcare systems have attempted to provide evidence-based mental health treatments, the availability and use of psychotherapies remains low. A significant need exists to identify simple but effective implementation strategies to adopt complex practices within complex systems of care. Emerging evidence suggests that facilitation may be an effective integrative implementation strategy for adoption of complex practices. The current pilot examined the use of external facilitation for adoption of cognitive behavioral therapy (CBT) in 20 Department of Veteran Affairs (VA) clinics.</p> <p>Methods</p> <p>The 20 clinics were paired on facility characteristics, and 23 clinicians from these were trained in CBT. A clinic in each pair was randomly selected to receive external facilitation. Quantitative methods were used to examine the extent of CBT implementation in 10 clinics that received external facilitation compared with 10 clinics that did not, and to better understand the relationship between individual providers' characteristics and attitudes and their CBT use. Costs of external facilitation were assessed by tracking the time spent by the facilitator and therapists in activities related to implementing CBT. Qualitative methods were used to explore contextual and other factors thought to influence implementation.</p> <p>Results</p> <p>Examination of change scores showed that facilitated therapists averaged an increase of 19% [95% CI: (2, 36)] in self-reported CBT use from baseline, while control therapists averaged a 4% [95% CI: (-14, 21)] increase. Therapists in the facilitated condition who were not providing CBT at baseline showed the greatest increase (35%) compared to a control therapist who was not providing CBT at baseline (10%) or to therapists in either condition who were providing CBT at baseline (average 3%). Increased CBT use was unrelated to prior CBT training. Barriers to CBT implementation were therapists' lack of control over their clinic schedule and poor communication with clinical leaders.</p> <p>Conclusions</p> <p>These findings suggest that facilitation may help clinicians make complex practice changes such as implementing an evidence-based psychotherapy. Furthermore, the substantial increase in CBT usage among the facilitation group was achieved at a modest cost.</p