Cities as Collective Beings

This article introduces the concepts of System, Autonomous System, Intelligent System, Multiple System, and Collective Being. It deals with issues related to managing these different levels of systemic aggregation. The author then discusses applications related to Architecture and design with particular reference to cities.     

Organisms,machines, and societies: From the vertical structure of adaptability to the management of information

The adaptability of organisms and human societies depends on the synergistic orchestration of processing modes at many different levels of organization, each with unique informational characteristics. Digital information technology is a narrow transect of this spectrum. Adaptability theory provides a tool for analyzing the interactions between these fundamentally different modes of processing and for delineating the factors that can lead to either positive or negative consequences for human life.     

Modeling of emotion elicitation conditions for a cognitive-emotive architecture

To cater to the need of embodying emotional behavior in an autonomous agent, there is a need for modeling computationally apt definitions of emotions. A number of emotion theories have been developed that provide an understanding of human psychology and their emotional behaviors, but it is difficult to directly decipher a theory into a computational model of emotion. Nevertheless, these theories together can serve as the theoretical foundation for designing a model for emotion-eliciting conditions. In this study, the salient features of OCC, Scherer, and Roseman theories of emotions are identified, which complement each other. The features are unified and standardized to bring consistency in deriving the computationally apt definition of five emotions viz. Happiness/Joy, Sadness, Fear, Anger, and Surprise. The objective of this hybridization is to set a ground framework for appraising the emotion-triggering cues (e.g., an event) for a simple, flexible and tolerant computational model of emotions. The underlying emotion-eliciting processes are designed using Fuzzy Logic. Fuzzy rules are framed to model the conditions behind emotion elicitation. Furthermore, the ISEAR data set and the real test-case scenarios are used to validate the accuracy of emotion prediction and rule fulfillment respectively.     

Towards learning agents with personality traits: Modeling Openness to Experience

Recent advances in neurosciences and cognitive sciences show us that the human neocortex is not a slave to the experiences from our perception and that the memories stored in hippocampus are goal weighted during the replay of the experiences for the purpose of re-learning from them. Temporal difference reinforcement learning systems that use neural networks as function approximators rely on an experience replay memory structure similar to the hippocampus. We bring forward this similarity and present a novel way of using a goal weighted prioritization of the memory that is biologically inspired. Furthermore, we introduce a novel prioritization criteria called Variety of Experience Index, or VEI, for weighting the selection of the experiences that are stored in the replay memory. Weighting the experiences based on two different extremes of VEI can behaviourally modify the agent’s learning process, generating different types of learning agents that exhibit different personality traits along the dimension of Openness to Experience.     

The semantic pointer theory of emotion: Integrating physiology,appraisal, and construction

Emotion theory needs to explain the relationship of language and emotions, and the embodiment of emotions, by specifying the computational mechanisms underlying emotion generation in the brain. We used Chris Eliasmith’s Semantic Pointer Architecture to develop POEM, a computational model that explains numerous important phenomena concerning emotions, including how some stimuli generate immediate emotional reactions, how some emotional reactions depend on cognitive evaluations, how bodily states influence the generation of emotions, how some emotions depend on interactions between physiological inputs and cognitive appraisals, and how some emotional reactions concern syntactically complex representations. We contrast our theory with current alternatives, and discuss some possible applications to individual and social emotions.     

Analysis and research on mental health of college students based on cognitive computing

In recent years, according to the survey, college students have frequent psychological problems, anxiety, depression, inferiority, interpersonal sensitivity and other psychological problems, and even more, even the idea of suicide. It has a very serious negative impact on the family and society. Children are the flowers of the motherland, and college students are the important cornerstone of the country's future development. Therefore, the mental health problems of college students are particularly important. Based on the research of cognitive computing, this paper combines the research data to analyze the influencing factors of mental health, analyzes the shortcomings of mental health education, and proposes corresponding ideas and solutions.     

Social networks and web minorities

The conception of appropriate models of information retrieval is a crucial step for the actual growth and development of the web. In addition to traditional information retrieval techniques, nowadays search engines have adopted methods for taking into account the social network deriving from the pattern of interconnections. Using different arguments, it has been shown that this is very effective in practice. In this paper we subscribe that point of view, but state also that social networks based ranking schemes operating on the whole web, like Google’s , prevent small communities to become visible on the Web, regardless of the quality of the information. Hence, we strongly advocate the development of new cognitive models of the whole process of information retrieval which are capable of facing this problem. We give a picture of distributed architectures which incorporates topic-based intelligent agents and personal agents for capturing the user’s profile.     

Multilayer cognitive architecture for UAV control

Extensive use of unmanned aerial vehicles (UAVs) in recent years has induced the rapid growth of research areas related to UAV production. Among these, the design of control systems capable of automating a wide range of UAV activities is one of the most actively explored and evolving. Currently, researchers and developers are interested in designing control systems that can be referred to as intelligent, e.g. the systems which are suited to solve such tasks as planning, goal prioritization, coalition formation, etc. and thus guarantee high levels of UAV autonomy. One of the principal problems in intelligent control system design is tying together various methods and models traditionally used in robotics and aimed at solving such tasks as dynamics modeling, control signal generation, location and mapping, path planning, etc. with the methods of behavior modeling and planning which are thoroughly studied in cognitive science. Our work is aimed at solving this problem. We propose layered architecture—STRL (strategic, tactical, reactive, layered)—of the control system that automates the behavior generation using a cognitive approach while taking into account complex dynamics and kinematics of the control object (UAV). We use a special type of knowledge representation—sign world model—that is based on the psychological activity theory to describe individual behavior planning and coalition formation processes. We also propose path planning methodology which serves as the mediator between the high-level cognitive activities and the reactive control signals generation. To generate these signals we use a state-dependent Riccati equation and specific method for solving it. We believe that utilization of the proposed architecture will broaden the spectrum of tasks which can be solved by the UAV’s coalition automatically, as well as raise the autonomy level of each individual member of that coalition.     

Visualizing the autonomous vehicle’s maneuvers – Does an ecological interface help to increase the hedonic quality and safety?

This paper examines whether ecological speed information describing ongoing driving maneuvers during automated driving enhances the hedonic quality and driving safety immediately after a driving takeover. Visualizing maneuvers and trajectories has already proven effective. However, planned acceleration and deceleration in an automated vehicle have not yet been investigated. Therefore, this paper assesses how an automated vehicle’s speed control information might be presented by an ecological interface. Besides a possible increase in the hedonic quality, this information might enhance safe behavior of the human driver when it comes to a takeover. To assess these two aspects, 43 drivers participated in a dynamic driving simulator study. Using a within-subject design, two scenarios were used to compare an ecological interface, dynamically visualizing speed changes, to a conventional pop-up interface, using pop-up icons to visualize speed changes. The experimental results indicate that ecological feedback and conventional pop-up feedback do not differ regarding the hedonic quality, which was reflected by the state anxiety, usefulness, and satisfaction with the overall human-machine interface (HMI). Nonetheless, the post-hoc questionnaire on situational awareness showed a significantly lower rating for the ecological interface which may be the result of a more automatic and subconscious processing of the information given. Analyzing the takeover performance, the initial takeover time was comparably low for both interfaces. However, concerning safety, the ecological interface significantly enhanced the lateral control after takeover, and the drivers looked at the vehicle mirrors significantly earlier. In conclusion, the results show that the information given by the ecological interface may help drivers cope with a sudden takeover in a faster and more controlled way. Future applications of these findings might serve to enhance the acceptance and safety of semi-autonomous vehicles by implementing ecological interfaces.     

Learning in LIDA

LIDA is a systems-level, biologically-inspired cognitive architecture. More than a decade of research on LIDA has seen much conceptual work on its learning mechanisms, and resulted in a set of conceptual commitments that constrain those mechanisms; perhaps the most essential of these constraints is the Conscious Learning Hypothesis from Global Workspace Theory, which asserts that all significant learning requires consciousness. Despite these successes, many conceptual challenges remain, and bridging the divide between LIDA’s conceptual model and its implementations has been challenging.The contributions of this paper are threefold: We present a detailed survey of learning in LIDA, during which we clarify, elaborate on, and synthesize together ideas from numerous papers, using updated terminology that reflects the continuing evolution of LIDA. We explore foundational issues in learning, such as, “What must be innate or built-in?” versus “What can be learned?”, the nature of LIDA’s representations, and the relationship between the LIDA conceptual model and its computational realizations. Finally, we provide a roadmap for future work. We believe that this paper will direct and catalyze our research endeavors, and provide a thorough introduction to the conceptual foundations of LIDA’s learning mechanisms that will be useful to anyone that would like a deeper understanding of LIDA or for those that plan to implement LIDA-based agents.