Visual perception of surface properties through manipulation

Vision is a component of a perceptual system whose function is to support purposeful behavior. In this project we studied the perceptual system that supports the visual perception of surface properties through manipulation. Observers were tasked with finding dents in simulated flat glossy surfaces. The surfaces were presented on a tangible display system implemented on an Apple iPad, that rendered the surfaces in real time and allowed observers to directly interact with them by tilting and rotating the device. On each trial we recorded the angular deviations indicated by the device's accelerometer and the images seen by the observer. The data reveal purposeful patterns of manipulation that serve the task by producing images that highlight the dent features. These investigations suggest the presence of an active visuo-motor perceptual system involved in the perception of surface properties, and provide a novel method for its study using tangible display systems

Planning polyphase behavior of autonomous intelligent mobile systems in uncertain environments

Introduction: We discuss the modern ways of developing intelligent problem solvers, focusing on their shortcomings in terms of the efficiency of their application for planning purposeful behavior of autonomous mobile intelligent systems in a priori undescribed conditions of a problem environment. Purpose: Developing a model of knowledge representation and processing which would provide the ways to organize purposeful activity of autonomous intelligent mobile systems in uncertain environment. Methods: Synthesis of frame-like behavior scenarios in the form of polyvariable conditionally dependent predicates whose structure includes complex variables as well as related variables of types “object”, “event” and “relationship”; synthesis of heuristic rules for knowledge representation in the process of purposeful behavior planning. In order to represent complex variables in polyvariable conditionally dependent predicates, fuzzy semantic networks are used which can represent knowledge of variously purposed intelligent systems without regard to particular knowledge domains, being adaptable to a priori undescribed operational conditions. Results: We have proposed a structure of various polyvariable conditionally dependent predicates. On their base, an autonomous intelligent mobile system can organize various activities in a priori undescribed and unstable problem environments. Specially developed knowledge processing tools allow such a system to automatically plan its purposeful behavior in a space of subtasks during the fulfilment of tasks formulated for it. Practical relevance: The obtained results can be efficiently used in building intelligent problem solvers for autonomous intelligent mobile systems of various purpose, capable of performing complex tasks in a priori undescribed operational conditions.

Socially robotic: making useless machines

As robots increasingly become part of our everyday lives, questions arise with regards to how to approach them and how to understand them in social contexts. The Western history of human–robot relations revolves around competition and control, which restricts our ability to relate to machines in other ways. In this study, we take a relational approach to explore different manners of socializing with robots, especially those that exceed an instrumental approach. The nonhuman subjects of this study are built to explore non-purposeful behavior, in an attempt to break away from the assumptions of utility that underlie the hegemonic human–machine interactions. This breakaway is accompanied by ‘learning to be attuned’ on the side of the human subjects, which is facilitated by continuous relations at the level of everyday life. Our paper highlights this ground for the emergence of meanings and questions that could not be subsumed by frameworks of control and domination. The research-creation project Machine Ménagerie serves as a case study for these ideas, demonstrating a relational approach in which the designer and the machines co-constitute each other through sustained interactions, becoming attuned to one another through the performance of research. Machine Ménagerie attempts to produce affective and playful—if not unruly—nonhuman entities that invite interaction yet have no intention of serving human social or physical needs. We diverge from other social robotics research by creating machines that do not attempt to mimic human social behaviours.

Polyvariable conditionally dependent predicates in the knowledge representation model of autonomous mobile intelligent agents

Показано, что известные модели представления и обработки знаний не позволяют построить интеллектуальный решатель задач автономных мобильных интеллектуальных агентов, способных выполнять сложные задания в априори неописанных нестабильных условиях проблемной среды. Для решения данной актуальной проблемы в статье предлагаются типовые конструкции модели представления знаний безотносительно к конкретной предметной области, строящиеся на основе полипеременных условно-зависимых предикатов. Приведена структура данного вида предикатов и определены условия, при выполнении которых, в результате означивания входящих в них различного сорта переменных, получаются истинные высказывания, характеризующие необходимые условия для достижения стоящих подцелей и целей поведения в текущей ситуации нестабильной проблемной среды. Разработаны различные по назначению типовые элементы модели представления знаний автономных интеллектуальных агентов, позволяющие формировать на их основе различные по сложности программы целенаправленной деятельности связанные с выполнением сформулированного им задания. Отмечено, что дальнейшее развитие полученных в настоящей работе результатов связано с формализацией мыслительных актов и разработкой инструментальных средств обработки знаний для построения алгоритмов автоматического планирования целенаправленного поведения автономных мобильных интеллектуальных агентов в нестабильных недоопределенных условиях функционирования. It is shown that the known models of knowledge representation and processing do not allow constructing an intelligent problem solver for autonomous mobile intelligent agents capable of performing complex tasks in a priori undescribed unstable conditions of a problematic environment. To solve this topical problem, the article proposes standard constructions of a knowledge representation model, without reference to a specific subject area, based on polyvariable conditionally dependent predicates. The structure of this type of predicates is given and the conditions are determined under which, as a result of the valuation of variables included in them, true statements are obtained that characterize the necessary conditions for achieving behavioral sub goals and goals in the current situation of an unstable problematic environment. The standard and different in purpose elements of knowledge representation model for autonomous intelligent agents have been developed, which make it possible to form programs of purposeful activity of different complexity associated with the implementation of the formulated task. It is noted that further development of the results obtained in this work is associated with the formalization of mental acts and the development of knowledge processing tools for constructing automatic planning algorithms of the purposeful behavior of autonomous mobile intelligent agents in unstable underdetermined conditions.

Instrumental Means for Managing the Rational Behavior of Self-Organizing Autonomous Intelligent Agents

We formulate the basic principles of constructing a sign-signal control for the expedient behavior of autonomous intelligent agents in a priori undescribed conditions of a problematic environment. We clarify the concept of a self-organizing autonomous intelligent agent as a system capable of automatic goal-setting when a certain type of conditional and unconditional signal — signs appears in a problem environment. The procedures for planning the expedient behavior of autonomous intelligent agents have been developed, that imitate trial actions under uncertainty in the process of studying the regularities of transforming situations in a problem environment, which allows avoiding environmental changes in the process of self-learning that are not related to the achievement of a given goal. Boundary estimates of the proposed procedures complexity for planning expedient behavior are determined, confirming the possibility of their effective implementation on the on-board computer of the automatic control system for the expedient activity of autonomous intelligent agents. We carry out an imitation on a personal computer of the proposed procedures for planning purposeful behavior, confirming the effectiveness of their use to build intelligent problem solvers for autonomous intelligent agents in order to endow them with the ability to adapt to a priori undescribed operating conditions. The main types of connections between various conditional and unconditional signal — signs of a problem environment are structured, which allows autonomous intelligent agents to adapt to complex a priori undescribed and unstable conditions of functioning.

Derivation of complex judgments as a conceptual tool thinking intelligent mobile systems

Обоснована целесообразность разработки инструментальных средств автоматического формирования суждений как одного из мыслительных актов понятийного мышления интеллектуальных мобильных систем различного назначения. Применение таких средств вывода суждений позволяет сформировать недостающие для принятия решений знания в процессе планирования целенаправленного поведения в априори неописанных условиях проблемной среды. Для решения данной проблемы в качестве исходных элементов представления знаний использованы условно – зависимые предикаты обеспечивающие возможность вывода как простых, так и сложных суждений определяющих различные закономерности целенаправленного преобразования текущих условий функционирования. Разработаны инструменты позволяющие формировать сложные суждения из простых суждений, полученных путем означивания переменных сорта «объекты» и «отношения» условно зависимых предикатов объектами, находящимися в проблемной среде и оценками отношений, которые наблюдаются в ней между данными объектами. В качестве примера показывающего эффективность использования предложенных инструментальных средств получения новых недостающих для вывода решений знаний, построен алгоритм планирования целенаправленного поведения автономного мобильного подводного робота обеспечивающий ему возможность выполнять достаточно сложные задания, связанные с поиском различных объектов, обладающих определенными свойствами и их перевозкой в заданную точку проблемной среды. The expediency of developing tools for automatic formation of judgments as one of the mental acts of conceptual thinking of intelligent mobile systems for various purposes has been substantiated. The use of such means of inference makes it possible to form the knowledge that is missing for decision-making in the process of planning purposeful behavior in a priori undescribed conditions of a problematic environment. To solve this problem, conditionally dependent predicates were used as the initial elements of knowledge representation, providing the ability to derive both simple and complex judgments that determine various patterns of purposeful transformation of the current conditions of functioning. Tools have been developed that allow to form complex judgments from simple judgments obtained by designating variables of the sort "objects" and "relations" of conditionally dependent predicates by objects located in a problematic environment and estimates of relations that are observed in it between these objects. As an example showing the effectiveness of using the proposed tools for obtaining new knowledge that is missing for the conclusion of solutions, an algorithm for planning the purposeful behavior of an autonomous mobile underwater robot has been built, which provides it with the ability to perform rather complex tasks related to the search for various objects with certain properties and their transportation to a given point of the problem Wednesday.

Divergent sensory processing converges in frontal cortex for a planned motor response

SUMMARYPurposeful behavior requires planning of actions based on external information. However, neuronal mechanisms converting sensory input into a motor plan remain elusive. Here, we combined wide-field calcium imaging, multi-area single-neuron recordings and focal optogenetic inactivation to reveal the precise sequence of cortical activity transforming sensory information into motor planning in mice trained to respond to a brief whisker stimulus by licking after a delay. We found that upon learning, the sensory information, initially highly-localized, rapidly spreads to diverse motor and higher-order areas, together with transient deactivation of orofacial regions, converging during the delay period to a focalized region of the frontal cortex. The secondary whisker motor cortex (wM2) appears as a key relay of this sensorimotor transformation, showing the earliest learning-enhanced response to the whisker stimulus. Our results suggest a specific cortical circuit with wM2 acquiring a pivotal role in transforming whisker information into preparatory activity for goal-directed motor planning.HighlightsCortex-wide, task-epoch specific causal neuronal dynamics of sensorimotor learningSensory information converges to a focal frontal region critical for delay-responseOrofacial cortex acquired an inhibitory response with delayed lick learningSecondary whisker motor cortex is a key node converting whisker input to lick plan

Predictors of Cognitive Defects and Its Prevention Capabilities in Premature Infants

The article summarizes the materials of modern publications on cognitive development of premature infants in connection with perinatal factors and parenting conditions. Leading risk predictors of cognitive defects in premature infants are severe dysmaturity by the time of birth (gestational age <27 weeks) and need for intensive care during the first weeks of life. The data of longitudinal researches of the premature infants’ development until reaching their adulthood is presented. The structure of cognitive defects in this population is studied. The most common problems were revealed in learning mathematics, operational memory and purposeful behavior and activity. Frequency of these cognitive defects is associated with both: stage of prematurity social problems of the family. Modern neurovisualization methods (diffusion weighted imaging and functional magnetic resonance imaging (MRI) of the brain) allows to identify the defects in child nervous system (Connectome) development already at the age of 18 months. It can be the substrate of cognitive defects, and it will allow to predict individual development pathway and implement direct corrections and interventions.

The Macy Conferences on Cybernetics: Reinstantiating the Mind

The Macy Conferences on Cybernetics were a series of 10 interdisciplinary scientific meetings that took place in New York between 1946 and 1953. The meetings were sponsored by the Macy Foundation, which aimed to promote interdisciplinary approaches to the social, behavioral, and medical sciences. Co-organized by neuropsychiatrist Warren S. McCulloch and Frank Fremont-Smith, medical director of the Macy Foundation, the meetings brought together a variety of scientists from mathematics, psychology, engineering, anthropology, physics, ecology, psychiatry, neurophysiology, linguistics, and sociology. The conferences strove to apply tools from the physical sciences and mathematics to problems in the biological and human sciences. Such tools stemmed first from Norbert Wiener’s work on the anti-aircraft predictor, in which he employed the concept of negative feedback to explain purposeful behavior, and second from McCulloch’s work with Walter Pitts on the logic of neural activity, which purported to embody logical reasoning in the physiology of the brain. Wiener and McCulloch touted the practice of hypothetical modelling as a bridge over the divide between the natural and the artificial, and a method for explaining purposeful behavior in organisms and machines. Discussions at the Macy Conferences expanded on this work, and participants discussed and debated models of cognitive functions such as sensation, communication, memory, and learning, all cast as functions of the mind and exemplars of purposeful behavior. Thus, the meetings signal a major shift in 20th-century psychology, when discussions of the mind took on a more central place in psychological discourse. Behaviorist psychologists in the early 20th century had largely rejected concepts of mind as unscientific and not objective. The Macy Conferences, in contrast, placed the mind at the nexus of interdisciplinary inquiry across the divide between the physical and human sciences, and helped to bring back the mind as a topic of objective, scientific inquiry in psychology and in the emerging cognitive sciences.

On the Task Approach in Artificial Intelligence and Cognitive Sciences

This work continues a series of publications on the task approach in artificial intelligence. As noted earlier, the agent-based approach described in the monograph by Stuart Russell and Pieter Norwig “Artificial Intelligence. The Modern Approach", may be more argumentatively presented within the framework of the task approach. This paper will show that not only the problems of the bases of mathematics and artificial intelligence, but also many cognitive functions performed by humans and analyzed in cognitive sciences, can also be described and studied within the framework of the task approach. In particular, this paper shows that the analogue of the concept of task in cognitive sciences is the concept of goal and that the Functional Systems Theory (FST), which describes purposeful behavior, can be presented as the brain's solution of tasks to achieve goals and satisfaction of needs. It gives the chance to compare directly the tasks of artificial intellect with natural cognitive processes and, thereby, to reveal the list of those tasks of "natural" intellect and schemes of their solution which can be successfully used for the solution of artificial intelligence tasks.