{"title":"Engineering Kindness: Building a Machine with Compassionate Intelligence","authors":"C. Mason","doi":"10.4018/IJSE.2015010101","DOIUrl":"https://doi.org/10.4018/IJSE.2015010101","url":null,"abstract":"The author provides first steps toward building a software agent/robot with compassionate intelligence. She approaches this goal with an example software agent, EM-2. She also gives a generalized software requirements guide for anyone wishing to pursue other means of building compassionate intelligence into an AI system. The purpose of EM-2 is not to build an agent with a state of mind that mimics empathy or consciousness, but rather to create practical applications of AI systems with knowledge and reasoning methods that positively take into account the feelings and state of self and others during decision making, action, or problem solving. To program EM-2 the author re-purposes code and architectural ideas from collaborative multi-agent systems and affective common sense reasoning with new concepts and philosophies from the human arts and sciences relating to compassion. EM-2 has predicates and an agent architecture based on a meta-cognition mental process that was used on India's worst prisoners to cultivate compassion for others, Vipassana or mindfulness. She describes and presents code snippets for common sense based affective inference and the I-TMS, an Irrational Truth Maintenance System, that maintains consistency in agent memory as feelings change over time, and provides a machine theoretic description of the consistency issues of combining affect and logic. The author summarizes the growing body of new biological, cognitive and immune discoveries about compassion and the consequences of these discoveries for programmers working with human-level AI and hybrid human-robot systems.","PeriodicalId":272943,"journal":{"name":"Int. J. Synth. Emot.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125960138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. D. Vries, P. Lemmens, D. Brokken, S. Pauws, Michael Biehl
{"title":"Towards Emotion Classification Using Appraisal Modeling","authors":"G. D. Vries, P. Lemmens, D. Brokken, S. Pauws, Michael Biehl","doi":"10.4018/IJSE.2015010103","DOIUrl":"https://doi.org/10.4018/IJSE.2015010103","url":null,"abstract":"The authors studied whether a two-step approach based on appraisal modeling could help in improving performance of emotion classification from sensor data that is typically executed in a one-stage approach in which sensor data is directly classified into a discrete emotion label. The proposed intermediate step is inspired by appraisal models in which emotions are characterized using appraisal dimensions, and subdivides the task in a person-dependent and person-independent stage. In this paper, the authors assessed feasibility of this second stage: the classification of emotion from appraisal data. They applied a variety of machine learning techniques and used visualization techniques to gain further insight into the classification task. Appraisal theory assumes the second step to be independent of the individual. Results obtained are promising, but do indicate that not all emotions can be equally well classified, perhaps indicating that the second stage is not as person-independent as proposed in the literature.","PeriodicalId":272943,"journal":{"name":"Int. J. Synth. Emot.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132814090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Comprehensive Study on Architecture of Neural Networks and Its Prospects in Cognitive Computing","authors":"S. Priyadarshini","doi":"10.4018/IJSE.2020070103","DOIUrl":"https://doi.org/10.4018/IJSE.2020070103","url":null,"abstract":"This paper proffers an overview of neural network, coupled with early neural network architecture, learning methods, and applications. Basically, neural networks are simplified models of biological nervous systems and that's why they have drawn crucial attention of research community in the domain of artificial intelligence. Basically, such networks are highly interconnected networks possessing a huge number of processing elements known as neurons. Such networks learn by examples and exhibit the mapping capabilities, generalization, fault resilience conjointly with escalated rate of information processing. In the current paper, various types of learning methods employed in case of neural networks are discussed. Subsequently, the paper details the deep neural network (DNN), its key concepts, optimization strategies, activation functions used. Afterwards, logistic regression and conventional optimization approaches are described in the paper. Finally, various applications of neural networks in various domains are included in the paper before concluding it.","PeriodicalId":272943,"journal":{"name":"Int. J. Synth. Emot.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127429457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Emotions and Information Processing: A Theoretical Approach","authors":"Ebrahim Oshni Alvandi","doi":"10.4018/jse.2011010101","DOIUrl":"https://doi.org/10.4018/jse.2011010101","url":null,"abstract":"An animate system standing in nature and trying to investigate its surroundings for different purposes does a type of cognitive processing. Emotions as mental states are leading human cognitive features that attract life by interactions processed in the world. This paper examines how this cognitive feature process works. By researching history and theories related to emotions and their generation, it becomes clear that information processing is discussed as a tool for their processes. Three different styles of information processing are evaluated for emotional processes. The pragmatic notion of information processing fits as a processing tool in modeling emotions and artificial emotions and explains the emotional process.","PeriodicalId":272943,"journal":{"name":"Int. J. Synth. Emot.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121168507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Density-Based Clustering Method for Trends Analysis Using Evolving Data Stream","authors":"Umesh Kokate, Arviand V. Deshpande, P. Mahalle","doi":"10.4018/IJSE.2020070102","DOIUrl":"https://doi.org/10.4018/IJSE.2020070102","url":null,"abstract":"Evolution of data in the data stream environment generates patterns at different time instances. The cluster formation changes with respect to time because of the behaviour and members of clusters. Data stream clustering (DSC) allows us to investigate the changes of the group behaviour. These changes in the behaviour of the group members over time lead to formation of new clusters and may make old clusters extinct. Also, these extinct old clusters may recur over time. The problem is to identify and record these change patterns of evolving data streams. The knowledge obtained from these change patterns is then used for trends analysis over evolving data streams. In order to address this flexible clustering requirement, density-based clustering method is proposed to dynamically cluster evolving data streams. The decay factor identifies formation of new clusters and diminishing of older clusters on arrival of data points. This indicates trends in evolving data streams.","PeriodicalId":272943,"journal":{"name":"Int. J. Synth. Emot.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114834122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Ethical Treatment of Robots and the Hard Problem of Robot Emotions","authors":"B. MacLennan","doi":"10.4018/ijse.2014010102","DOIUrl":"https://doi.org/10.4018/ijse.2014010102","url":null,"abstract":"Emotions are important cognitive faculties that enable animals to behave intelligently in real time. The author argues that many important current and future applications of autonomous robots will require them to have a rich emotional repertoire, but this raises the question of whether it is possible for robots to experience their emotions consciously, as people do. Under what conditions would phenomenal experience of emotions be possible for robots? This is, in effect, the \"hard problem\" of robot emotions. This paper outlines a scientific approach to the question grounded in experimental neurophenomenology.","PeriodicalId":272943,"journal":{"name":"Int. J. Synth. Emot.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122934673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automatic, Dimensional and Continuous Emotion Recognition","authors":"H. Gunes, M. Pantic","doi":"10.4018/jse.2010101605","DOIUrl":"https://doi.org/10.4018/jse.2010101605","url":null,"abstract":"Recognition and analysis of human emotions have attracted a lot of interest in the past two decades and have been researched extensively in neuroscience, psychology, cognitive sciences, and computer sciences. Most of the past research in machine analysis of human emotion has focused on recognition of prototypic expressions of six basic emotions based on data that has been posed on demand and acquired in laboratory settings. More recently, there has been a shift toward recognition of affective displays recorded in naturalistic settings as driven by real world applications. This shift in affective computing research is aimed toward subtle, continuous, and context-specific interpretations of affective displays recorded in real-world settings and toward combining multiple modalities for analysis and recognition of human emotion. Accordingly, this article explores recent advances in dimensional and continuous affect modeling, sensing, and automatic recognition from visual, audio, tactile, and brain-wave modalities.","PeriodicalId":272943,"journal":{"name":"Int. J. Synth. Emot.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124145046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Novel Nature Instilled Moving Sink Architecture for Data Gathering in Wireless Sensor Networks","authors":"Amiya Bhusan Bagjadab, S. Priyadarshini","doi":"10.4018/ijse.2020010104","DOIUrl":"https://doi.org/10.4018/ijse.2020010104","url":null,"abstract":"Wireless sensor networks are commonly used to monitor certain regions and to collect data for several application domains. Generally, in wireless sensor networks, data are routed in a multi-hop fashion towards a static sink. In this scenario, the nodes closer to the sink become heavily involved in packet forwarding, and their battery power is exhausted rapidly. This article proposes that a special node (i.e., mobile sink) will move in the specified region and collect the data from the sensors and transmit it to the base station such that the communication distance of the sensors will be reduced. The aim is to provide a track for the sink such that it covers maximum sensor nodes. Here, the authors compared two tracks theoretically and in the future will try to simulate the two tracks for the sink movement so as to identify the better one.","PeriodicalId":272943,"journal":{"name":"Int. J. Synth. Emot.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129016596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Network Traffic Intrusion Detection System Using Fuzzy Logic and Neural Network","authors":"M. Dixit, Rajashwini Ukarande","doi":"10.4018/IJSE.2017010101","DOIUrl":"https://doi.org/10.4018/IJSE.2017010101","url":null,"abstract":"","PeriodicalId":272943,"journal":{"name":"Int. J. Synth. Emot.","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126217277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On Integration Linguistic Factors to Fuzzy Similarity Measures and Intuitionistic Fuzzy Similarity Measures","authors":"P. Phong, V. T. Hue","doi":"10.4018/ijse.2019010101","DOIUrl":"https://doi.org/10.4018/ijse.2019010101","url":null,"abstract":"The article is concerned with integrating linguistic elements into fuzzy similarity measures and intuitionistic fuzzy similarity measure. Some new concepts are proposed: a fuzzy linguistic value (FLv), a fuzzy linguistic vector (FLV), an intuitionistic fuzzy linguistic vector (ILV) and similarity measures. The proposed measures are used to build classification algorithms. As predicted theoretically, experiments show that with the same type of similarity measures, the linguistic-aggregated similarity measures produce better results in classification problems.","PeriodicalId":272943,"journal":{"name":"Int. J. Synth. Emot.","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133785656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}