scholarly journals A Multidisciplinary Artificial Intelligence Model of an Affective Robot

10.5772/45662 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 6 ◽  
Author(s):  
Hooman Aghaebrahimi Samani ◽  
Elham Saadatian
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Network ◽  
Multidisciplinary Approach ◽  
Affective State ◽  
Dynamic Bayesian Network ◽  
Endocrine System ◽  
Affective States ◽  
Intelligence System ◽  
Human Love ◽  
High Level

A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelligence of the system includes three modules, namely Probabilistic Love Assembly (PLA), based on the psychology of love, Artificial Endocrine System (AES), based on the physiology of love, and Affective State Transition (AST), based on emotions. The PLA module employs a Bayesian network to incorporate psychological parameters of affection in the robot. The AES module employs artificial emotional and biological hormones via a Dynamic Bayesian Network (DBN). The AST module uses a novel transition method for handling affective states of the robot. These three modules work together to manage emotional behaviours of the robot.

scholarly journals The Use of Artificial Intelligence in Assessing Affective States in Livestock

2021 ◽  
Vol 8 ◽  
Author(s):  
Suresh Neethirajan
Keyword(s):  
Artificial Intelligence ◽  
Affective Computing ◽  
Multidisciplinary Approach ◽  
Farm Animals ◽  
Affective States ◽  
Indirect Measures ◽  
Detection Of Stress ◽  
Production Animals ◽  
Self Learning ◽  
Labor Consuming

In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.

scholarly journals Affective States and Performance Outcomes – The Findings of Preliminary Research Involving Pentathletes

2016 ◽  
Vol 23 (1) ◽  
pp. 21-24
Author(s):  
Aleksandra Samełko ◽  
Monika Guszkowska
Keyword(s):  
Female Athletes ◽  
Mood State ◽  
Affective State ◽  
Performance Outcomes ◽  
Emotional States ◽  
Affective States ◽  
State Variables ◽  
And Performance ◽  
High Level ◽  
The Relationship

Abstract Introduction. The aim of this article is to discuss the relationship between affective states experienced by athletes and the outcome of their performance. The article presents the findings of a pilot study which made it possible to determine the relationship between the emotional states, mood, and level of stress of a group of pentathletes and the outcomes they achieved in a sports competition. Material and methods. The study involved 12 senior modern pentathletes, including 7 male and 5 female athletes. The following standard psychology questionnaires were used in the study: the 10-item Perceived Stress Scale (PSS-10), the Positive and Negative Affect Schedule (PANAS), and the Profile of Mood State (POMS). Performance was assessed based on the number of points achieved by the pentathletes in particular events in the pentathlon, which are held according to the rules set by the International Modern Pentathlon Union (UIPM). Results. The findings of the study confirmed that there was a correlation between the athletes’ mood and emotions and the outcome of their performance. The level of stress strongly negatively correlated with both the outcome they expected to achieve and the one they actually achieved for the combined event (running and shooting). For this event a relationship was also found between the athletes’ affective states and their outcomes: in running and shooting there was a positive and statistically significant correlation between the level of positive emotions and anger and the results achieved. However, friendliness, one of the other affective state variables that were measured, correlated negatively with the outcomes of the athletes’ performance. Conclusions. In the group of pentathletes who participated in the study, a high level of anger was associated with better outcomes, and a high level of friendliness had an adverse effect on the results achieved. The findings of the current study confirm that there is a relationship between affective states and performance outcomes, but the findings do not correspond with Morgan’s iceberg profile.

scholarly journals AISpace2: An Interactive Visualization Tool for Learning and Teaching Artificial Intelligence

2020 ◽  
Vol 34 (09) ◽  
pp. 13436-13443
Author(s):  
Chenliang Zhou ◽  
Dominic Kuang ◽  
Jingru Liu ◽  
Hanbo Yang ◽  
Zijia Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Network ◽  
Open Source ◽  
Constraint Satisfaction ◽  
Interactive Visualization ◽  
Constraint Satisfaction Problems ◽  
Graphical Form ◽  
Visualization Tool ◽  
Learning And Teaching ◽  
High Level

AIspace is a set of tools used to learn and teach fundamental AI algorithms. The original version of AIspace was written in Java. There was not a clean separation of the algorithms and visualization; it was too complicated for students to modify the underlying algorithms. Its next generation, AIspace2, is built on AIPython, open source Python code that is designed to be as close as possible to pseudocode. AISpace2, visualized in JupyterLab, keeps the simple Python code, and uses hooks in AIPython to allow visualization of the algorithms. This allows students to see and modify the high-level algorithms in Python, and to visualize the output in a graphical form, aiming to better help them to build confidence and comfort in AI concepts and algorithms. So far we have tools for search, constraint satisfaction problems (CSP), planning and Bayesian network. In this paper we outline the tools and give some evaluations based on user feedback.

scholarly journals Concept to recognize crisis of organizational and institutional separation by Artificial Intelligence System

2021 ◽  
Vol 10 (43) ◽  
pp. 59-71
Author(s):  
Oleg N. Dmitriev ◽  
Veronika A. Zolotova
Keyword(s):  
Artificial Intelligence ◽  
Crisis Management ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Critical Management ◽  
High Level ◽  
Post Industrial ◽  
Typological Classification ◽  
Crisis Situations

The sphere of anti-crisis management is highlighted in relation to the open variety of organizational and institutional separations that are typical for the higher forms of industrial and post-industrial economies. This article shows the typicity and relevance of critical management situations associated with the emergence of crises. Furthermore, it justifies the objective orientation to a dense (not sparse) stream of crisis situations requiring identification, ranking, and classification. A strict management interpretation of the separation crisis is given through an assessment of the nature of the dynamics of the separation state indexes. Also, the document presents a generalized typological classification of crises. This article shows the necessity of using a high-level Artificial Intelligence System for this purpose, an indispensable component of which is the classification component.

Europe

2020 ◽  
pp. 649-666
Author(s):  
Andrea Renda
Keyword(s):  
Artificial Intelligence ◽  
Digital Technology ◽  
Policy Framework ◽  
Expert Group ◽  
Ethical Guidelines ◽  
Ethical Implications ◽  
Strong Focus ◽  
Ethics Guidelines ◽  
Definition Of ◽  
High Level

This chapter assesses Europe’s efforts in developing a full-fledged strategy on the human and ethical implications of artificial intelligence (AI). The strong focus on ethics in the European Union’s AI strategy should be seen in the context of an overall strategy that aims at protecting citizens and civil society from abuses of digital technology but also as part of a competitiveness-oriented strategy aimed at raising the standards for access to Europe’s wealthy Single Market. In this context, one of the most peculiar steps in the European Union’s strategy was the creation of an independent High-Level Expert Group on AI (AI HLEG), accompanied by the launch of an AI Alliance, which quickly attracted several hundred participants. The AI HLEG, a multistakeholder group including fifty-two experts, was tasked with the definition of Ethics Guidelines as well as with the formulation of “Policy and Investment Recommendations.” With the advice of the AI HLEG, the European Commission put forward ethical guidelines for Trustworthy AI—which are now paving the way for a comprehensive, risk-based policy framework.

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