Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-265
Maj Stenmark, J. Malec
When robots are working in dynamic environments, close to humans lacking extensive knowledge of robotics, there is a strong need to simplify the user interaction and make the system execute as autonomously as possible. For industrial robots working side-by-side with humans in manufacturing industry, AI systems are necessary to lower the demand on programming time and expertise. We are convinced that only by building a system with appropriate knowledge and reasoning services, we can simplify the robot programming sufficiently to meet those demands and still get a robust and efficient task execution. In this paper, we present a system we have realized that aims at fulfilling the above demands. The paper focuses on the ontologies we have created for robotic devices and manufacturing tasks, and presents examples of AI-related services using the semantic descriptions of the skills to help the user instruct the robot adequately. (Less)
{"title":"Knowledge-Based Industrial Robotics","authors":"Maj Stenmark, J. Malec","doi":"10.3233/978-1-61499-330-8-265","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-265","url":null,"abstract":"When robots are working in dynamic environments, close to humans lacking extensive knowledge of robotics, there is a strong need to simplify the user interaction and make the system execute as autonomously as possible. For industrial robots working side-by-side with humans in manufacturing industry, AI systems are necessary to lower the demand on programming time and expertise. We are convinced that only by building a system with appropriate knowledge and reasoning services, we can simplify the robot programming sufficiently to meet those demands and still get a robust and efficient task execution. In this paper, we present a system we have realized that aims at fulfilling the above demands. The paper focuses on the ontologies we have created for robotic devices and manufacturing tasks, and presents examples of AI-related services using the semantic descriptions of the skills to help the user instruct the robot adequately. (Less)","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"54 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":"114546896","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-754-3-187
J. Cassens, Anders Kofod-Petersen
When designing and implementing real world ambient intelligent systems, we are in need of applicable information systems engineering methods. These should supplement the knowledge engineering tools we can find in the intelligent systems area. The work presented here focuses on explanation-aware ambient intelligent systems. The ability to explain it’s reasoning and actions has been identified as one core capability of any intelligent entity [1]. The question of what is considered a good explanation is context dependent [2], leading to the necessity to design the explanatory capabilities of an ambient intelligent system together with the contextual modelling. We target the requirements elicitation, analysis, and specification processes by making use of a pattern-based approach in form of Jackson’s problem frames [3]. His set of basic problem frames can be extended to be better able to model domain specific aspects. We have previously suggested additional problem frames for explanatory capabilities [4].
{"title":"Extended Abstract: Modelling Explanation-Aware Ambient Intelligent Systems with Problem Frames","authors":"J. Cassens, Anders Kofod-Petersen","doi":"10.3233/978-1-60750-754-3-187","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-187","url":null,"abstract":"When designing and implementing real world ambient intelligent systems, we are in need of applicable information systems engineering methods. These should supplement the knowledge engineering tools we can find in the intelligent systems area. The work presented here focuses on explanation-aware ambient intelligent systems. The ability to explain it’s reasoning and actions has been identified as one core capability of any intelligent entity [1]. The question of what is considered a good explanation is context dependent [2], leading to the necessity to design the explanatory capabilities of an ambient intelligent system together with the contextual modelling. We target the requirements elicitation, analysis, and specification processes by making use of a pattern-based approach in form of Jackson’s problem frames [3]. His set of basic problem frames can be extended to be better able to model domain specific aspects. We have previously suggested additional problem frames for explanatory capabilities [4].","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"117 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":"116381413","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-15
A. Ammar, Zied Elouedi, P. Lingras
{"title":"Decremental Possibilistic K-Modes","authors":"A. Ammar, Zied Elouedi, P. Lingras","doi":"10.3233/978-1-61499-330-8-15","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-15","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"18 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":"123645590","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-155
Uwe Köckemann, F. Pecora, L. Karlsson
The real-world applicability of automated planners depends on the expressiveness of the problem modeling language. Contemporary planners can deal with causal features of the problem, but only limited forms of temporal, resource and relational constraints. These constraints should be fully supported for dealing with real-world applications. We propose a highly-expressive, action-based planning language which includes causal, relational, temporal and resource constraints. This paper also contributes an approach for solving such rich planning problems by decomposition and constraint reasoning. The approach is general with respect to the types of constraints used in the problem definition language, in that additional solvers need only satisfy certain formal properties. The approach is evaluated on a domain which utilizes many features offered by the introduced language.
{"title":"Expressive Planning Through Constraints","authors":"Uwe Köckemann, F. Pecora, L. Karlsson","doi":"10.3233/978-1-61499-330-8-155","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-155","url":null,"abstract":"The real-world applicability of automated planners depends on the expressiveness of the problem modeling language. Contemporary planners can deal with causal features of the problem, but only limited forms of temporal, resource and relational constraints. These constraints should be fully supported for dealing with real-world applications. We propose a highly-expressive, action-based planning language which includes causal, relational, temporal and resource constraints. This paper also contributes an approach for solving such rich planning problems by decomposition and constraint reasoning. The approach is general with respect to the types of constraints used in the problem definition language, in that additional solvers need only satisfy certain formal properties. The approach is evaluated on a domain which utilizes many features offered by the introduced language.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"18 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":"125524693","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-205
Sławomir Nowaczyk, Rune Prytz, Thorsteinn S. Rögnvaldsson, S. Byttner
Predictive maintenance is becoming more and more important for the commercial vehicle manufactures, as focus shifts from product- to service-based operation. The idea is to provide a dynamic mainte ...
{"title":"Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data","authors":"Sławomir Nowaczyk, Rune Prytz, Thorsteinn S. Rögnvaldsson, S. Byttner","doi":"10.3233/978-1-61499-330-8-205","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-205","url":null,"abstract":"Predictive maintenance is becoming more and more important for the commercial vehicle manufactures, as focus shifts from product- to service-based operation. The idea is to provide a dynamic mainte ...","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","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":"129781482","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-589-0-68
A. S. Jensen, Christian Kaysø-Rørdam, J. Villadsen
. In real-time strategy games players make decisions and control their units simultaneously . Players are required to make decisions under time pressure and should be able to control multiple units at once in order to be successful. We present the design and implementation of a multi-agent interface for the real-time strategy game S TAR C RAFT : B ROOD W AR . This makes it possible to build agents that control each of the units in a game. We make use of the Environment Interface Standard , thus enabling different agent programming languages to use our interface, and we show how agents can control the units in the game in the Jason and GOAL agent programming languages.
{"title":"Interfacing Agents to Real-Time Strategy Games","authors":"A. S. Jensen, Christian Kaysø-Rørdam, J. Villadsen","doi":"10.3233/978-1-61499-589-0-68","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-68","url":null,"abstract":". In real-time strategy games players make decisions and control their units simultaneously . Players are required to make decisions under time pressure and should be able to control multiple units at once in order to be successful. We present the design and implementation of a multi-agent interface for the real-time strategy game S TAR C RAFT : B ROOD W AR . This makes it possible to build agents that control each of the units in a game. We make use of the Environment Interface Standard , thus enabling different agent programming languages to use our interface, and we show how agents can control the units in the game in the Jason and GOAL agent programming languages.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"29 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":"125394460","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-589-0-7
Shaibal Barua, S. Begum, Mobyen Uddin Ahmed
Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving.This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well.For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%.
{"title":"Clustering based Approach for Automated EEG Artifacts Handling","authors":"Shaibal Barua, S. Begum, Mobyen Uddin Ahmed","doi":"10.3233/978-1-61499-589-0-7","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-7","url":null,"abstract":"Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving.This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well.For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"8 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":"125051620","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-754-3-122
A. Tidemann, F. O. Bjørnson, A. Aamodt
Fish farmers manage assets of considerable value on a daily basis. Many aspects of the daily operation are automated in some way, such as the feeding sys- tem. Sensory equipment steadily becomes cheaper and more ubiquitous, yielding data that can be used by automated systems and for post-processing (i.e. data min- ing) to discover hidden trends in the data. However, a lot of information is only known informally by the fish farmers themselves, through years of experience. Companies that can store this information and reuse it will have an advantage; even more so if high-level human expertise can be linked to low-level sensor data. This paper presents early developments of a system that stores this informal knowledge using case based-reasoning, combined with corresponding sensor data.
{"title":"Case-Based Reasoning in a System Architecture for Intelligent Fish Farming","authors":"A. Tidemann, F. O. Bjørnson, A. Aamodt","doi":"10.3233/978-1-60750-754-3-122","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-122","url":null,"abstract":"Fish farmers manage assets of considerable value on a daily basis. Many aspects of the daily operation are automated in some way, such as the feeding sys- tem. Sensory equipment steadily becomes cheaper and more ubiquitous, yielding data that can be used by automated systems and for post-processing (i.e. data min- ing) to discover hidden trends in the data. However, a lot of information is only known informally by the fish farmers themselves, through years of experience. Companies that can store this information and reuse it will have an advantage; even more so if high-level human expertise can be linked to low-level sensor data. This paper presents early developments of a system that stores this informal knowledge using case based-reasoning, combined with corresponding sensor data.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"8 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":"126398374","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-589-0-147
Mattias Tiger, F. Heintz
Learning to recognize common activities such as traffic activities and robot behavior is an important and challenging problem related both to AI and robotics. We propose an unsupervised approach th ...
{"title":"Towards Unsupervised Learning, Classification and Prediction of Activities in a Stream-Based Framework","authors":"Mattias Tiger, F. Heintz","doi":"10.3233/978-1-61499-589-0-147","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-147","url":null,"abstract":"Learning to recognize common activities such as traffic activities and robot behavior is an important and challenging problem related both to AI and robotics. We propose an unsupervised approach th ...","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","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":"130513248","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-185
Hazar Mliki, Nesrine Fourati, Mohamed Hammami, H. Ben-Abdallah
In this paper, we introduce a new facial-expression analysis system designed to automatically recognize facial expressions, able to manage facial-expression intensity variation as well as reducing the doubt and confusion between facial-expression classes. Our proposed approach introduces a new method to segment efficiently facial feature contours using Vector Field Convolution (VFC) technique. Relying on the detected con- tours, we extract facial feature points which go with facial-expression deformations. Then we have modeled a set of distances among the detected points to define prediction rules through data mining technique. An experimental study was conducted to evaluate the per- formance of our proposed solution under varying factors.
{"title":"Data Mining-based Facial Expressions Recognition System","authors":"Hazar Mliki, Nesrine Fourati, Mohamed Hammami, H. Ben-Abdallah","doi":"10.3233/978-1-61499-330-8-185","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-185","url":null,"abstract":"In this paper, we introduce a new facial-expression analysis system designed to automatically recognize facial expressions, able to manage facial-expression intensity variation as well as reducing the doubt and confusion between facial-expression classes. Our proposed approach introduces a new method to segment efficiently facial feature contours using Vector Field Convolution (VFC) technique. Relying on the detected con- tours, we extract facial feature points which go with facial-expression deformations. Then we have modeled a set of distances among the detected points to define prediction rules through data mining technique. An experimental study was conducted to evaluate the per- formance of our proposed solution under varying factors.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":" 83","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120827609","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}
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