Characterization of Indicators for Adaptive Human-Swarm Teaming

Swarm systems consist of large numbers of agents that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from urban search and rescue situations to cyber defence. However, the successful deployment of the swarm in such applications is conditioned by the effective coupling between human and swarm. While adaptive autonomy promises to provide enhanced performance in human-machine interaction, distinct factors must be considered for its implementation within human-swarm interaction. This paper reviews the multidisciplinary literature on different aspects contributing to the facilitation of adaptive autonomy in human-swarm interaction. Specifically, five aspects that are necessary for an adaptive agent to operate properly are considered and discussed, including mission objectives, interaction, mission complexity, automation levels, and human states. We distill the corresponding indicators in each of the five aspects, and propose a framework, named MICAH (i.e., Mission-Interaction-Complexity-Automation-Human), which maps the primitive state indicators needed for adaptive human-swarm teaming

Cognitive Behavioral Therapy versus Short Psychodynamic Supportive Psychotherapy in the outpatient treatment of depression: a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Previous research has shown that Short Psychodynamic Supportive Psychotherapy (SPSP) is an effective alternative to pharmacotherapy and combined treatment (SPSP and pharmacotherapy) in the treatment of depressed outpatients. The question remains, however, how Short Psychodynamic Supportive Psychotherapy compares with other established psychotherapy methods. The present study compares Short Psychodynamic Supportive Psychotherapy to the evidence-based Cognitive Behavioral Therapy in terms of acceptability, feasibility, and efficacy in the outpatient treatment of depression. Moreover, this study aims to identify clinical predictors that can distinguish patients who may benefit from either of these treatments in particular. This article outlines the study protocol. The results of the study, which is being currently carried out, will be presented as soon as they are available.</p> <p>Methods/Design</p> <p>Adult outpatients with a main diagnosis of major depressive disorder or depressive disorder not otherwise specified according to DSM-IV criteria and mild to severe depressive symptoms (<it>Hamilton Depression Rating Scale </it>score ≥ 14) are randomly allocated to Short Psychodynamic Supportive Psychotherapy or Cognitive Behavioral Therapy. Both treatments are individual psychotherapies consisting of 16 sessions within 22 weeks. Assessments take place at baseline (week 0), during the treatment period (week 5 and 10) and at treatment termination (week 22). In addition, a follow-up assessment takes place one year after treatment start (week 52). Primary outcome measures are the number of patients refusing treatment (acceptability); the number of patients terminating treatment prematurely (feasibility); and the severity of depressive symptoms (efficacy) according to an independent rater, the clinician and the patient. Secondary outcome measures include general psychopathology, general psychotherapy outcome, pain, health-related quality of life, and cost-effectiveness. Clinical predictors of treatment outcome include demographic variables, psychiatric symptoms, cognitive and psychological patient characteristics and the quality of the therapeutic relationship.</p> <p>Discussion</p> <p>This study evaluates Short Psychodynamic Supportive Psychotherapy as a treatment for depressed outpatients by comparing it to the established evidence-based treatment Cognitive Behavioral Therapy. Specific strengths of this study include its strong external validity and the clinical relevance of its research aims. Limitations of the study are discussed.</p> <p>Trial registration</p> <p>Current Controlled Trails ISRCTN31263312</p

A survey of search-based refactoring for software maintenance

This survey reviews published materials relating to the specific area of Search Based Software Engineering concerning software maintenance. 99 papers are selected from online databases to analyze and review the area of Search Based Software Maintenance. The literature addresses different methods to automate the software maintenance process. There are studies that analyze different software metrics, studies that experiment with multi-objective techniques and papers that propose refactoring tools for use. This survey also suggests papers from related areas of research, and introduces some of the concepts and techniques used in the area. The current state of the research is analyzed in order to assess opportunities for future research. This survey is beneficial as an introduction for any researchers aiming to work in the area of Search Based Software Maintenance and will allow them to gain an understanding of the current landscape of the research and the insights gathered. The papers reviewed as well as the refactoring tools introduced are tabulated in order to aid researchers in quickly referencing studies

Computational Red Teaming: Risk Analytics of Big-Data-to-Decisions Intelligent Systems

This book introduces readers to the concepts and methods of Computational Red Teaming (CRT)

A Survey of Learning Classifier Systems in Games

Games are becoming increasingly indispensable, not only for fun but also to support tasks that are more serious, such as education, strategic planning, and understanding of complex phenomena. Computational intelligence-based methods are contributing significantly to this development. Learning Classifier Systems (LCS) is a pioneering computational intelligence approach that combines machine learning methods with evolutionary computation, to learn problem solutions in the form of interpretable rules. These systems offer several advantages for game applications, including a powerful and flexible agent architecture built on a knowledge-based symbolic modeling engine; modeling flexibility that allows integrating domain knowledge and different machine learning mechanisms under a single computational framework; an ability to adapt to diverse game requirements; and an ability to learn and generate creative agent behaviors in real-time dynamic environments. We present a comprehensive and dedicated survey of LCS in computer games. The survey highlights the versatility and advantages of these systems by reviewing their application in a variety of games. The survey is organized according to a general game classification and provides an opportunity to bring this important research direction into the public eye. We discuss the strengths and weaknesses of the existing approaches and provide insights into important future research directions

Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Air Traffic Control (ATC) is a complex safety critical environment. A tower controller would be making many decisions in real-time to sequence aircraft. While some optimization tools exist to help the controller in some airports, even in these situations, the real sequence of the aircraft adopted by the controller is significantly different from the one proposed by the optimization algorithm. This is due to the very dynamic nature of the environment. The objective of this paper is to test the hypothesis that one can learn from the sequence adopted by the controller some strategies that can act as heuristics in decision support tools for aircraft sequencing. This aim is tested in this paper by attempting to learn sequences generated from a well-known sequencing method that is being used in the real world. The approach relies on a genetic algorithm (GA) to learn these sequences using a society Probabilistic Finite-state Machines (PFSMs). Each PFSM learns a different sub-space; thus, decomposing the learning problem into a group of agents that need to work together to learn the overall problem. Three sequence metrics (Levenshtein, Hamming and Position distances) are compared as the fitness functions in GA. As the results suggest, it is possible to learn the behavior of the algorithm/heuristic that generated the original sequence from very limited information

2016 14th Annual Conference on Privacy, Security and Trust (PST)

Human identity is a prerequisite for trust assurance and assessment, which is essential for effective human-machine interaction in trusted autonomous systems. Unlike conventional authentication methods which do not require users to re-authenticate themselves for sustained access, continuous authentication affirms human identity in real-time, therefore is a solution for continued access monitoring in trusted autonomous systems. Robust continuous authentication needs robust multi-modal data sources. In this paper, we design a multi-modal biometrics system that continuously verifies the presence of a logged-in user. Two types of biometric data are used, face images and Electroencephalography (EEG) signals. Information from individual modalities is fused at matching score level. For face modality, matching scores are calculated by distances between eigenface coefficients. While for EEG signals, an event-related potential (ERP) modality is established by a simple ERP elicitation protocol and calculation of cross-correlation similarities. Scores from the two modalities are normalized and fused using three schemes, namely the sum-score, max-score and min-score scheme. The experiments reveal that individual variations found in the ERPs are detectable and can be used for continuous authentication. This is an interesting finding which indicates that the ERP biometrics are feasible for user authentication and worthy of further research. Results also show that combining ERP biometric with face biometric using sum-score scheme outperforms each modality in isolation. This piece of finding indicates the potential of integrating ERP into multimodal authentication systems

Networking the Boids Is More Robust Against Adversarial Learning

Swarm behavior using Boids-like models has been studied primarily using close-proximity spatial sensory information (e.g., vision range). In this study, we propose a novel approach in which the classic definition of boids' neighborhood that relies on sensory perception and Euclidian space locality is replaced with graph-theoretic network-based proximity mimicking communication and social networks. We demonstrate that networking the boids leads to faster swarming and higher quality of the formation. We further investigate the effect of adversarial learning, whereby an observer attempts to reverse engineer the dynamics of the swarm through observing its behavior. The results show that networking the swarm demonstrated a more robust approach against adversarial learning than local-proximity neighborhood structure

Visual and auditory reaction time for air traffic controllers using quantitative electroencephalograph (QEEG) data

The use of quantitative electroencephalograph in the analysis of air traffic controllers' performance can reveal with a high temporal resolution those mental responses associated with different task demands. To understand the relationship between visual and auditory correct responses, reaction time, and the corresponding brain areas and functions, air traffic controllers were given an integrated visual and auditory continuous reaction task. Strong correlations were found between correct responses to the visual target and the theta band in the frontal lobe, the total power in the medial of the parietal lobe and the theta-to-beta ratio in the left side of the occipital lobe. Incorrect visual responses triggered activations in additional bands including the alpha band in the medial of the frontal and parietal lobes, and the Sensorimotor Rhythm in the medial of the parietal lobe. Controllers' responses to visual cues were found to be more accurate but slower than their corresponding performance on auditory cues. These results suggest that controllers are more susceptible to overload when more visual cues are used in the air traffic control system, and more errors are pruned as more auditory cues are used. Therefore, workload studies should be carried out to assess the usefulness of additional cues and their interactions with the air traffic control environment