15,253 research outputs found
CoachAI: A Conversational Agent Assisted Health Coaching Platform
Poor lifestyle represents a health risk factor and is the leading cause of
morbidity and chronic conditions. The impact of poor lifestyle can be
significantly altered by individual behavior change. Although the current shift
in healthcare towards a long lasting modifiable behavior, however, with
increasing caregiver workload and individuals' continuous needs of care, there
is a need to ease caregiver's work while ensuring continuous interaction with
users. This paper describes the design and validation of CoachAI, a
conversational agent assisted health coaching system to support health
intervention delivery to individuals and groups. CoachAI instantiates a text
based healthcare chatbot system that bridges the remote human coach and the
users. This research provides three main contributions to the preventive
healthcare and healthy lifestyle promotion: (1) it presents the conversational
agent to aid the caregiver; (2) it aims to decrease caregiver's workload and
enhance care given to users, by handling (automating) repetitive caregiver
tasks; and (3) it presents a domain independent mobile health conversational
agent for health intervention delivery. We will discuss our approach and
analyze the results of a one month validation study on physical activity,
healthy diet and stress management
Behavioral Aspects of Organizational Learning and Adaptation
In this paper, I seek to understand the behavioral basis of higher organizational learning and adaption as a teleological dynamic equilibrium process to decipher the underlying psycho-physiological aspects of individual cognitive learning related to organizational adaption. Dynamics of cognitive learning has some differential paths within the neural circuitry which follows certain patterns that leads to individual as well as organized evolution in course of a learning process. I undertake a comparative analysis of human cognitive and behavioral changes and the active mechanisms underlying animal behavior and learning processes to understand the differential patterns of these adaptive changes in these two species. Cognitive behavioral learning processes have certain economic perspectives which help an individual to attain efficiency in workplace adaptation and in learning which however, the individual when being part of an alliance, ember positive influence on the society or organization as a whole. Comparatively, in primates, I review some empirical evidences drawn from chronological studies about cognitive behavioral learning process and adaptation as well as the presence of the capacity of making attributions about mental states, which exists in rudimentary form in chimpanzees and apes. Following this, I apply the outcomes of the findings on different aspects of human cognitive and adaptive behavioral learning-induced evolutionary changes and how human beings are able to exploit the presence of these additive advantages under cluster settings.Animal behavior, cognitive economics, motivational energy, neural adaptation, neuroscience, Organizational learning, organizational adaptation, teleological process
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Cocaine Addiction as a Homeostatic Reinforcement Learning Disorder
Drug addiction implicates both reward learning and homeostatic regulation mechanisms of the brain. This has stimulated 2 partially successful theoretical perspectives on addiction. Many important aspects of addiction, however, remain to be explained within a single, unified framework that integrates the 2 mechanisms. Building upon a recently developed homeostatic reinforcement learning theory, the authors focus on a key transition stage of addiction that is well modeled in animals, escalation of drug use, and propose a computational theory of cocaine addiction where cocaine reinforces behavior due to its rapid homeostatic corrective effect, whereas its chronic use induces slow and long-lasting changes in homeostatic setpoint. Simulations show that our new theory accounts for key behavioral and neurobiological features of addiction, most notably, escalation of cocaine use, drug-primed craving and relapse, individual differences underlying dose-response curves, and dopamine D2-receptor downregulation in addicts. The theory also generates unique predictions about cocaine self-administration behavior in rats that are confirmed by new experimental results. Viewing addiction as a homeostatic reinforcement learning disorder coherently explains many behavioral and neurobiological aspects of the transition to cocaine addiction, and suggests a new perspective toward understanding addiction
The organisation of sociality: a manifesto for a new science of multi-agent systems
In this paper, we pose and motivate a challenge, namely the need for a new science of multi-agent systems. We propose that this new science should be grounded, theoretically on a richer conception of sociality, and methodologically on the extensive use of computational modelling for real-world applications and social simulations. Here, the steps we set forth towards meeting that challenge are mainly theoretical. In this respect, we provide a new model of multi-agent systems that reflects a fully explicated conception of cognition, both at the individual and the collective level. Finally, the mechanisms and principles underpinning the model will be examined with particular emphasis on the contributions provided by contemporary organisation theory
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Homeostatic reinforcement learning for integrating reward collection and physiological stability
Efficient regulation of internal homeostasis and defending it against perturbations requires adaptive behavioral strategies. However, the computational principles mediating the interaction between homeostatic and associative learning processes remain undefined. Here we use a definition of primary rewards, as outcomes fulfilling physiological needs, to build a normative theory showing how learning motivated behaviors may be modulated by internal states. Within this framework, we mathematically prove that seeking rewards is equivalent to the fundamental objective of physiological stability, defining the notion of physiological rationality of behavior. We further suggest a formal basis for temporal discounting of rewards by showing that discounting motivates animals to follow the shortest path in the space of physiological variables toward the desired setpoint. We also explain how animals learn to act predictively to preclude prospective homeostatic challenges, and several other behavioral patterns. Finally, we suggest a computational role for interaction between hypothalamus and the brain reward system
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