Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges

Intelligent escape is an interdisciplinary field that employs artificial intelligence (AI) techniques to enable robots with the capacity to intelligently react to potential dangers in dynamic, intricate, and unpredictable scenarios. As the emphasis on safety becomes increasingly paramount and advancements in robotic technologies continue to advance, a wide range of intelligent escape methodologies has been developed in recent years. This paper presents a comprehensive survey of state-of-the-art research work on intelligent escape of robotic systems. Four main methods of intelligent escape are reviewed, including planning-based methodologies, partitioning-based methodologies, learning-based methodologies, and bio-inspired methodologies. The strengths and limitations of existing methods are summarized. In addition, potential applications of intelligent escape are discussed in various domains, such as search and rescue, evacuation, military security, and healthcare. In an effort to develop new approaches to intelligent escape, this survey identifies current research challenges and provides insights into future research trends in intelligent escape.Comment: This paper is accepted by Journal of Intelligent and Robotic System

Corporate Behavior and the Social Efficiency of Tort Law

This article examines this dissonance between accepted theory and observed reality, between what the model envisions and what the tort system seems to deliver. After sketching the model in greater detail, the first section of the article reviews restraints within tort law on the achievement of efficient outcomes. The analysis then turns to the broader legal environment, and describes how legally sanctioned means of liability evasion - such as the corporate law doctrine of limited liability and the bankruptcy rules permitting discharge of obligations - may further undermine the practical utility of the social efficiency model of tort. The final section of the article examines tort reform\u27s potential for overcoming such barriers to efficiency, and, in light of its pessimistic conclusion, suggests that rethinking the efficiency norm may be a more appropriate response

Parallel evolutionary programming techniques for strategy optimisation in air combat scenarios

Air combat between fighter missiles and aircraft can be categorised as a pursuit-evasion problem. One aircraft acts as a pursuer and the other as an evader. Generally, the pursuer will try to capture the evader as quickly as possible and the evader tries to evade capture for as long as possible. For an experienced human pilot, it is trivial to discuss this methodology, but to simulate it, the mathematics involved is very complex and difficult to implement in a computer environment. Classical methods, though very accurate in their analysis, are not suited to solve a complex 6DOF pursuit-evasion problem and they have limitations in representing real-world problems such as discontinuities, discrete, stochastic, chaotic, temporal information or lack of information. In this thesis, evolutionary programming (EP) is applied to determine the optimum maneuvering strategy for an aircraft (evader) to avoid interception by an incoming missile (pursuer). EP is a class of algorithms known as Evolutionary Algorithm (EA). EA has an ability to find an optimal solution in a complex problem which involves discontinuities, discrete, nondifferential parameters and noise. In addition, the methodology was implemented on parallel computer architecture to improve the computing time and expanding the search space. A sensitivity analysis was carried out to determine the best configuration and to understand the effect of parameters, such as number of processors, population size, number of generations, etc., on the results. The effects of sensor and instrument errors were also considered. The method enabled feasible solutions to be found in a relatively short period of time. However, the ability to search for feasible solutions is dependent on various parameters such as initial conditions, aircraft configurations and aerodynamic constraints. It is concluded that, in general, EP is able to determine feasible maneuvering strategies for an evader to avoid interception with and without instrument errors. The methodology has the potential to be used as a training tool for pilots in air combat or as an intelligent engagement strategy for autonomous systems, such as Unmanned Air Combat Vehicles (UCAV)

On the role and opportunities in teamwork design for advanced multi-robot search systems

Intelligent robotic systems are becoming ever more present in our lives across a multitude of domains such as industry, transportation, agriculture, security, healthcare and even education. Such systems enable humans to focus on the interesting and sophisticated tasks while robots accomplish tasks that are either too tedious, routine or potentially dangerous for humans to do. Recent advances in perception technologies and accompanying hardware, mainly attributed to rapid advancements in the deep-learning ecosystem, enable the deployment of robotic systems equipped with onboard sensors as well as the computational power to perform autonomous reasoning and decision making online. While there has been significant progress in expanding the capabilities of single and multi-robot systems during the last decades across a multitude of domains and applications, there are still many promising areas for research that can advance the state of cooperative searching systems that employ multiple robots. In this article, several prospective avenues of research in teamwork cooperation with considerable potential for advancement of multi-robot search systems will be visited and discussed. In previous works we have shown that multi-agent search tasks can greatly benefit from intelligent cooperation between team members and can achieve performance close to the theoretical optimum. The techniques applied can be used in a variety of domains including planning against adversarial opponents, control of forest fires and coordinating search-and-rescue missions. The state-of-the-art on methods of multi-robot search across several selected domains of application is explained, highlighting the pros and cons of each method, providing an up-to-date view on the current state of the domains and their future challenges

Optimizing evasive strategies for an evader with imperfect vision capacity

The multiagent pursuit-evasion problem has attracted considerable interest during recent years, and a general assumption is that the evader has perfect vision capacity. However, in the real world, the vision capacity of the evader is always imperfect, and it may have noisy observation within its limited field of view. Such an imperfect vision capacity makes the evader sense incomplete and inaccurate information from the environment, and thus, the evader will achieve suboptimal decisions. To address this challenge, we decompose this problem into two subproblems: 1) optimizing evasive strategies with a limited field of view, and 2) optimizing evasive strategies with noisy observation. For the evader with a limited field of view, we propose a memory-based ‘worst case’ algorithm, the idea of which is to store the locations of the pursuers seen before and estimate the possible region of the pursuers outside the sight of the evader. For the evader with noisy observation, we propose a value-based reinforcement learning algorithm that trains the evader offline and applies the learned strategy to the actual environment, aiming at reducing the impact of uncertainty created by inaccurate information. Furthermore, we combine and make a trade-off between the above two algorithms and propose a memory-based reinforcement learning algorithm that utilizes the estimated locations to modify the input of the state set in the reinforcement learning algorithm. Finally, we extensively evaluate our algorithms in simulation, concluding that in this imperfect vision capacity setting, our algorithms significantly improve the escape success rate of the evader.This work was supported by the National Natural Science Foundation of China (61472079, 61170164, 61807008 and 61806053), the Natural Science Foundation of Jiangsu Province of China (BK20171363, BK20180356, BK20180369, BK20170693)

Machine Medical Ethics

In medical settings, machines are in close proximity with human beings: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. Machines in these contexts are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for empathy and emotion detection necessary? What about consciousness? The essays in this collection by researchers from both humanities and science describe various theoretical and experimental approaches to adding medical ethics to a machine, what design features are necessary in order to achieve this, philosophical and practical questions concerning justice, rights, decision-making and responsibility, and accurately modeling essential physician-machine-patient relationships. This collection is the first book to address these 21st-century concerns

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Understanding and supporting end-user debugging strategies

End users' programs are fraught with errors, costing companies millions of dollars. One reason may be that researchers and tool designers have not yet focused on end-user debugging strategies. To investigate this possibility, this dissertation presents eight empirical studies and a new strategy-based end-user debugging tool for Excel, called StratCel. These studies revealed insights about males' and females' end-user debugging strategies at four levels of abstraction (moves, tactics, stratagems, and strategies), leading to specific implications for the design of tools. Particular results include a set of ten debugging stratagems, which generalized across three environments: the research spreadsheet environment Forms/3, commercial Windows PowerShell, and commercial Microsoft Excel. There was also evidence of the stratagems' generalization to a fourth set of environments: interactive application design environments, such as Dreamweaver, Flash, and Blend. Males and females statistically preferred and were effective with different stratagems, and females' stratagems were less well-supported by environments than males' were. In addition to what stratagems our participants used, we also investigated how these stratagems were used to find bugs, fix bugs, and evaluate fixes. Furthermore, a Sensemaking approach revealed end-user debugging strategies and the advantages and disadvantages of each. We then built StratCel, which demonstrates a strategy-centric approach to tool design by explicitly supporting several of the implications from our studies. StratCel's evaluation revealed significant benefits of a strategy-centric approach to tool design: participants using the tool found twice as many bugs as participants using standard Excel and fixed four times as many bugs (including two bugs which had not been purposefully inserted by researchers and had gone unnoticed in previous studies). Further, StratCel participants did so much faster than participants using standard Excel. For example, participants using StratCel found the first bug 90% faster and fixed it 80% faster than participants without the tool. Finally, this strategy-based approach helped the participants who needed it the most: boosting novices' debugging performance near experienced participants' improved levels. The fact that this approach helped everyone -- males and females, novices and experts —- demonstrates the significant advantage of strategy-centric approaches over feature-centric approaches to designing tools that aim to support end-user debugging