/pdf/remote-sensing-of-the-environment-4yfxjaaswe.pdf

Remote sensing of the environment

which is used to describe a third component of thinking processes, particularly preverbal, and it denotes the concept that the world is activated by some generalized "energy" that links together causally all objects and events ; it is presumably revealed by a person's lack of curiosity about causal connections, as though they were self-evident. Aside from the rather frequent use of such key words, having strong connotations for this reviewer far away from what the author is aiming to denote, the book is written in a lucid and stimulating style that makes reading it an invigorating intellectual exercise. It is a book that is likely to have somewhat limited attraction to the full-time clinician, especially one treating adult patients. And child psychiatrists and psychologists, if reasonably well read, will most likely be familiar with the majority of references from which this author has synthesized his material. On the other hand, the scholarly and refreshing con¬ ceptual approaches of the author will appeal to psychologists, philosophers, linguists, and psychiatrists with a research bent and anyone else who wants to be provoked to do some thinking on the problems of language, language development, and the psychology of cognition. Louis A. GOTTSCHALK, M.D. Plans and the Structure of Behavior. By George A Miller, Eugene gALANTER, and Karl H. Pribram. Price, $5.00. Pp. 226. Henry Holt & Co., Inc., New York 17, 1960. This is an important book for psychiatrists and behavioral scientists, since it presents a clear, concise study of the application of cybernetics, information and computer theory to the problem of analyzing behavior. The authors have been actively engaged in behavioral research in different areas\p=m-\Millerin information and communication, Galanter in experimental psychology, and Pribram in neurophysiology. The book resulted from a series of discussions which they engaged in during a year they spent together at the Center for Advanced Study in the Behavioral Sciences, Palo Alto, Calif. Their original intent was to write a diary, as it were, of the development of their ideas and, fortunately, enough of this remains to make the book clear, easy to read, and interesting. It is also fortunate, however, that in the final writing a variety of studies comparing the "behavior" of computing machines with human "cognitive behavior" have been reviewed and summarized. The result is one of the best presentations of the present status of the brain-computer problem. The authors, however, do not discuss certain aspects of this problem, such as the difference between the brain and its vast number of parallel channels, but few operations, and the modern high-speed computer with its few channels and vast numbers of operations. This omission is consistent with their interest, since it would introduce the question of mechanisms rather than the problem of the structure of behavior as it is observed in everyday life in the clinic and in experiments on learning, conditioning, etc. Similarly, they do not discuss the qualitative differences between mechanisms of memory in the computer and those in the brain. In the former, a "memory"\p=m-\ i.e., stored information\p=m-\isidentified, metaphorically speaking, by an address, whereas no such mechanism is known in the brain (personal communication, Dr. Julian Bigelow). With few exceptions, however, the data, concepts, and theories presented are handled with elegant precision, as illustrated by the discussion of Sherrington's concepts of the "Reflex" and the "Synapse," Kurt Lewin's ideas of "tension states," and the numerous references to the work of Newell, Shaw, and Simon on computers and logic. There are, nevertheless, areas with

https://link.springer.com/content/pdf/10.1057%2Fjors.1968.86.pdf

Plans and the Structure of Behavior

A fundamental aspect of autonomous vehicle guidance is planning trajectories. Historically, two fields have contributed to trajectory or motion planning methods: robotics and dynamics and control. The former typically have a stronger focus on computational issues and real-time robot control, while the latter emphasize the dynamic behavior and more specific aspects of trajectory performance. Guidance for Unmanned Aerial Vehicles (UAVs), including fixed- and rotary-wing aircraft, involves significant differences from most traditionally defined mobile and manipulator robots. Qualities characteristic to UAVs include non-trivial dynamics, three-dimensional environments, disturbed operating conditions, and high levels of uncertainty in state knowledge. Otherwise, UAV guidance shares qualities with typical robotic motion planning problems, including partial knowledge of the environment and tasks that can range from basic goal interception, which can be precisely specified, to more general tasks like surveillance and reconnaissance, which are harder to specify. These basic planning problems involve continual interaction with the environment. The purpose of this paper is to provide an overview of existing motion planning algorithms while adding perspectives and practical examples from UAV guidance approaches.

A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance

Recently, there has been growing interest in developing unmanned aircraft systems (UAS) with advanced onboard autonomous capabilities. This paper describes the current state of the art in autonomous rotorcraft UAS (RUAS) and provides a detailed literature review of the last two decades of active research on RUAS. Three functional technology areas are identified as the core components of an autonomous RUAS. Guidance, navigation, and control (GNC) have received much attention from the research community, and have dominated the UAS literature from the nineties until now. This paper first presents the main research groups involved in the development of GNC systems for RUAS. Then it describes the development of a framework that provides standard definitions and metrics characterizing and measuring the autonomy level of a RUAS using GNC aspects. This framework is intended to facilitate the understanding and the organization of this survey paper, but it can also serve as a common reference for the UAS community. The main objective of this paper is to present a comprehensive survey of RUAS research that captures all seminal works and milestones in each GNC area, with a particular focus on practical methods and technologies that have been demonstrated in flight tests. These algorithms and systems have been classified into different categories and classes based on the autonomy level they provide and the algorithmic approach used. Finally, the paper discusses the RUAS literature in general and highlights challenges that need to be addressed in developing autonomous systems for unmanned rotorcraft. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems

Innovation Diffusion as a Spatial Process

Networked dynamical systems are increasingly used as models for a variety of processes ranging from robotic teams to collections of genetically engineered living cells. As the complexity of these systems increases, so does the range of emergent properties that they exhibit. In this work, we define a new logic called Spatial-Temporal Logic (SpaTeL) that is a unification of signal temporal logic (STL) and tree spatial superposition logic (TSSL). SpaTeL is capable of describing high-level spatial patterns that change over time, e.g., "Power consumption in the northwest quadrant of the city drops below 100 megawatts if the power consumption in the southwest quadrant remains above 200 megawatts for two hours." We present a statistical model checking procedure that evaluates the probability with which a networked system satisfies a SpaTeL formula. We also develop a synthesis procedure that determines system parameters maximizing the average degree of satisfaction, a continuous measure that quantifies how strongly a system execution satisfies a given formula. We demonstrate our algorithms on two systems: a biochemical reaction-diffusion system and a demand-side management system for a smart neighborhood.

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SpaTeL: a novel spatial-temporal logic and its applications to networked systems

This paper addresses the problem of learning optimal policies for satisfying signal temporal logic (STL) specifications by agents with unknown stochastic dynamics. The system is modeled as a Markov decision process, in which the states represent partitions of a continuous space and the transition probabilities are unknown. We formulate two synthesis problems where the desired STL specification is enforced by maximizing the probability of satisfaction, and the expected robustness degree, that is, a measure quantifying the quality of satisfaction. We discuss that Q-learning is not directly applicable to these problems because, based on the quantitative semantics of STL, the probability of satisfaction and expected robustness degree are not in the standard objective form of Q-learning. To resolve this issue, we propose an approximation of STL synthesis problems that can be solved via Q-learning, and we derive some performance bounds for the policies obtained by the approximate approach. The performance of the proposed method is demonstrated via simulations.

Q-Learning for robust satisfaction of signal temporal logic specifications

This paper presents an inference algorithm that can discover temporal logic properties of a system from data. Our algorithm operates on finite time system trajectories that are labeled according to whether or not they demonstrate some desirable system properties (e.g. "the car successfully stops before hitting an obstruction"). A temporal logic formula that can discriminate between the desirable behaviors and the undesirable ones is constructed. The formulae also indicate possible causes for each set of behaviors (e.g. "If the speed of the car is greater than 15 m/s within 0.5s of brake application, the obstruction will be struck") which can be used to tune designs or to perform on-line monitoring to ensure the desired behavior. We introduce reactive parameter signal temporal logic (rPSTL), a fragment of parameter signal temporal logic (PSTL) that is expressive enough to capture causal, spatial, and temporal relationships in data. We define a partial order over the set of rPSTL formulae that is based on language inclusion. This order enables a directed search over this set, i.e. given a candidate rPSTL formula that does not adequately match the observed data, we can automatically construct a formula that will fit the data at least as well. Two case studies, one involving a cattle herding scenario and one involving a stochastic hybrid gene circuit model, are presented to illustrate our approach.

/pdf/temporal-logic-inference-for-classification-and-prediction-a8lzd2hx1g.pdf

Temporal logic inference for classification and prediction from data

The increased complexity of modern systems necessitates automated anomaly detection methods to detect possible anomalous behavior determined by malfunctions or external attacks. We present formal methods for inferring (via supervised learning) and detecting (via unsupervised learning) anomalous behavior. Our procedures use data to construct a signal temporal logic (STL) formula that describes normal system behavior. This logic can be used to formulate properties such as “If the train brakes within 500 m of the platform at a speed of 50 km/hr, then it will stop in at least 30 s and at most 50 s.” Our procedure infers not only the physical parameters involved in the formula (e.g., 500 m in the example above) but also its logical structure. STL gives a more human-readable representation of behavior than classifiers represented as surfaces in high-dimensional feature spaces. The learned formula enables us to perform early detection by using monitoring techniques and anomaly mitigation by using formal synthesis techniques. We demonstrate the power of our methods with examples of naval surveillance and a train braking system.

Zhaodan Kong

Papers

A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance

SpaTeL: a novel spatial-temporal logic and its applications to networked systems

Q-Learning for robust satisfaction of signal temporal logic specifications

Temporal logic inference for classification and prediction from data

Temporal Logics for Learning and Detection of Anomalous Behavior