M. Cossentino, David E. Guastella, S. Lopes, L. Sabatucci, M. Tripiciano
Plans for emergency response are complex collaborations in which actors take roles and responsibilities. They are generally long textual documents containing practical instructions, in natural language, for hazard responses. A more rigorous structured-text would be useful for a twofold audience. From one side, it can be useful for quickly understanding the plan and on the other side it can be used to improve the modelling phase and delivering an automatic emergency-support system. This paper proposes an approach, conceived for humans, for converting a free-form plan document into a structured version of the same document. The approach is based on a linguistic and semantic analysis that are strictly correlated and materialize in a metamodel. It contains the essential elements of an emergency plan, and it aids in interpreting the input document also reducing inconsistencies, redundancies, and ambiguities.
{"title":"Linguistic and semantic layers for emergency plans","authors":"M. Cossentino, David E. Guastella, S. Lopes, L. Sabatucci, M. Tripiciano","doi":"10.3233/ia-210122","DOIUrl":"https://doi.org/10.3233/ia-210122","url":null,"abstract":"Plans for emergency response are complex collaborations in which actors take roles and responsibilities. They are generally long textual documents containing practical instructions, in natural language, for hazard responses. A more rigorous structured-text would be useful for a twofold audience. From one side, it can be useful for quickly understanding the plan and on the other side it can be used to improve the modelling phase and delivering an automatic emergency-support system. This paper proposes an approach, conceived for humans, for converting a free-form plan document into a structured version of the same document. The approach is based on a linguistic and semantic analysis that are strictly correlated and materialize in a metamodel. It contains the essential elements of an emergency plan, and it aids in interpreting the input document also reducing inconsistencies, redundancies, and ambiguities.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":"16 1","pages":"7-25"},"PeriodicalIF":1.5,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45733292","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}
Recent years have witnessed the rise of accurate but obscure classification models that hide the logic of their internal decision processes. In this paper, we present a framework to locally explain any type of black-box classifiers working on any data type through a rule-based model. In the literature already exists local explanation approaches able to accomplish this task. However, they suffer from a significant limitation that implies representing data as a binary vectors and constraining the local surrogate model to be trained on synthetic instances that are not representative of the real world. We overcome these deficiencies by using autoencoder-based approaches. The proposed framework first allows to generate synthetic instances in the latent feature space and learn a latent decision tree classifier. After that, it selects and decodes the synthetic instances respecting local decision rules. Independently from the data type under analysis, such synthetic instances belonging to different classes can unveil the reasons for the classification. Also, depending on the data type, they can be exploited to provide the most useful kind of explanation. Experiments show that the proposed framework advances the state-of-the-art towards a comprehensive and widely usable approach that is able to successfully guarantee various properties besides interpretability.
{"title":"Exploiting auto-encoders for explaining black-box classifiers","authors":"Riccardo Guidotti","doi":"10.3233/ia-220139","DOIUrl":"https://doi.org/10.3233/ia-220139","url":null,"abstract":"Recent years have witnessed the rise of accurate but obscure classification models that hide the logic of their internal decision processes. In this paper, we present a framework to locally explain any type of black-box classifiers working on any data type through a rule-based model. In the literature already exists local explanation approaches able to accomplish this task. However, they suffer from a significant limitation that implies representing data as a binary vectors and constraining the local surrogate model to be trained on synthetic instances that are not representative of the real world. We overcome these deficiencies by using autoencoder-based approaches. The proposed framework first allows to generate synthetic instances in the latent feature space and learn a latent decision tree classifier. After that, it selects and decodes the synthetic instances respecting local decision rules. Independently from the data type under analysis, such synthetic instances belonging to different classes can unveil the reasons for the classification. Also, depending on the data type, they can be exploited to provide the most useful kind of explanation. Experiments show that the proposed framework advances the state-of-the-art towards a comprehensive and widely usable approach that is able to successfully guarantee various properties besides interpretability.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":"16 1","pages":"115-129"},"PeriodicalIF":1.5,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45449024","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. Cicala, S. Demarchi, Marco Menapace, Leopoldo Annunziata, A. Tacchella
Like other custom-built machinery, elevators are charecterized by a design process which includes selection, sizing and placement of components to fit a given configuration, satisfy users’ requirements and adhere to stringent normative regulations. Unlike mass-produced items, the design process needs to be repeated almost from scratch each time a new configuration is considered. Since elevators are still designed mostly manually, project engineers must engage in time-consuming and error-prone activities over and over again, leaving little to be reused from one design to the next. Computer automated design can provide a cost-effective solution as it relieves the project engineer from such burdens. However, it introduces new challenges both in terms of efficiency — the search space for solutions grows exponentially in the number of component choices — and effectiveness — the perceived quality of the final design may not be as good as in the manual process. In this paper we compare three mainstream AI techniques that can provide problem-solving capabilities inside our tool LiftCreate for automated elevator design, namely Genetic Algorithms (GAs), Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). A special-purpose heuristic search technique embedded in LiftCreate provides us with a yardstick to evaluate the solutions obtained with GAs, CP and SMT and to assess their feasibility for practical applications.
{"title":"A comparison of declarative AI techniques for computer automated design of elevator systems","authors":"G. Cicala, S. Demarchi, Marco Menapace, Leopoldo Annunziata, A. Tacchella","doi":"10.3233/ia-210132","DOIUrl":"https://doi.org/10.3233/ia-210132","url":null,"abstract":"Like other custom-built machinery, elevators are charecterized by a design process which includes selection, sizing and placement of components to fit a given configuration, satisfy users’ requirements and adhere to stringent normative regulations. Unlike mass-produced items, the design process needs to be repeated almost from scratch each time a new configuration is considered. Since elevators are still designed mostly manually, project engineers must engage in time-consuming and error-prone activities over and over again, leaving little to be reused from one design to the next. Computer automated design can provide a cost-effective solution as it relieves the project engineer from such burdens. However, it introduces new challenges both in terms of efficiency — the search space for solutions grows exponentially in the number of component choices — and effectiveness — the perceived quality of the final design may not be as good as in the manual process. In this paper we compare three mainstream AI techniques that can provide problem-solving capabilities inside our tool LiftCreate for automated elevator design, namely Genetic Algorithms (GAs), Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). A special-purpose heuristic search technique embedded in LiftCreate provides us with a yardstick to evaluate the solutions obtained with GAs, CP and SMT and to assess their feasibility for practical applications.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":"16 1","pages":"131-150"},"PeriodicalIF":1.5,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41525695","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}
Carmelo Fabio Longo, C. Santoro, Marianna Nicolosi Asmundo, Domenico Cantone, Daniele Francesco Santamaria
The interoperability of devices from distinct brands on the Internet of Things (IoT) domain is still an open issue. The main reason is that pioneer companies always deliberately neglected to deploy devices able to interoperate with competitors products. The key factors that may invert such a trend derive, on one hand, from the abstraction of communication protocols that facilitates the migration from vertical to horizontal paradigms and, on the other hand, from the introduction of common and shared ontologies encoding devices specifications. The Semantic Web, with all its layers, can be considered the main framework for delivering ontologies, and by virtue of its features, it is surely the ideal means for providing shared knowledge. In this paper we present a framework that instantiates cognitive agents operating in IoT context, endowed with meta-reasoning in the Semantic Web. The framework, called SW-Caspar, is also provided with a module that performs semi-automatic ontology learning from sentences expressed in natural language; such a learning process generates a conceptual space reflecting the domain of discourse with an instance of a novel foundational ontology called Linguistic Oriented Davidsonian Ontology (LODO), whose main feature is to increase the deepness of reasoning without compromising linguistic-related features. LODO is inspired by the First-Order Logic Davidsonian notation and is serialized in OWL 2. Well-known examples derived from the theory of logical reasoning and a case-study applied to automation on health scenarios are also provided.
{"title":"Towards ontological interoperability of cognitive IoT agents based on natural language processing","authors":"Carmelo Fabio Longo, C. Santoro, Marianna Nicolosi Asmundo, Domenico Cantone, Daniele Francesco Santamaria","doi":"10.3233/ia-210125","DOIUrl":"https://doi.org/10.3233/ia-210125","url":null,"abstract":"The interoperability of devices from distinct brands on the Internet of Things (IoT) domain is still an open issue. The main reason is that pioneer companies always deliberately neglected to deploy devices able to interoperate with competitors products. The key factors that may invert such a trend derive, on one hand, from the abstraction of communication protocols that facilitates the migration from vertical to horizontal paradigms and, on the other hand, from the introduction of common and shared ontologies encoding devices specifications. The Semantic Web, with all its layers, can be considered the main framework for delivering ontologies, and by virtue of its features, it is surely the ideal means for providing shared knowledge. In this paper we present a framework that instantiates cognitive agents operating in IoT context, endowed with meta-reasoning in the Semantic Web. The framework, called SW-Caspar, is also provided with a module that performs semi-automatic ontology learning from sentences expressed in natural language; such a learning process generates a conceptual space reflecting the domain of discourse with an instance of a novel foundational ontology called Linguistic Oriented Davidsonian Ontology (LODO), whose main feature is to increase the deepness of reasoning without compromising linguistic-related features. LODO is inspired by the First-Order Logic Davidsonian notation and is serialized in OWL 2. Well-known examples derived from the theory of logical reasoning and a case-study applied to automation on health scenarios are also provided.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":"16 1","pages":"93-112"},"PeriodicalIF":1.5,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47783920","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}
Autonomy is crucial in cooperation. The complexity of HRI scenarios requires autonomous robots able to exploit their superhuman computations (based on DNN, Machine Learning techniques and Big Data) in a trustworthy way. Trustworthiness is not only a matter of accuracy, privacy or security, but it is becoming more and more a matter of adaptation to humans agency. As claimed by Falcone and Castelfranchi, autonomy means the possibility of dislaying or providing an unexpected behavior (including refusal) that departs from a requested (agreed upon or not) behavior. In this sense, the autonomy to decide how to adopt a task delegated by the user, with respect to her/his own real needs and goals, distinguishes intelligent and trustworthy robots from highly performing robots. This kind of smart help can be provided only by cognitive robots able to represent and ascribe mental states (beliefs, goals, intentions, desires etc.) to their interlocutors. The mental states attribution can be the result of complex reasoning mechanisms or can be fast and automatic, based on scripts, roles, categories or stereotypes typically exploited by humans every time they interact in everyday life. In all these cases, robots that build and use cognitive models of humans (that have a Theory of Mind of their interlocutors), have to operate also a meta-evaluation of their own predictive skills to build those models. Robots have to be endowed with the capability to self-trust their skills to interpret the interlocutors and the context, for producing smart and effective decisions towards humans. After exploring the main concepts that make collaboration between humans and robots trustworthy and effective, we present the first of a series of experiments draw for testing different aspects of a designed cognitive architecture for trustworthy HRI. This architecture, based on consolidated theoretical principles (theory of social adjustable autonomy, theory of mind, theory of trust) has the main goal to build cognitive robots that provide smart, trustworthy collaboration, every time a human requires their help. In particular, the experiment has been designed in order to demonstrate how the robot’s capability to learn its own level of self-trust on its predictive abilities in perceiving the user and building a model of her/him, allows it to establish a trustworthy collaboration and to maintain a high level of user’s satisfaction, with respect to the robot’s performance, also when these abilities progressively degrade.
{"title":"Human-robot interaction through adjustable social autonomy","authors":"Filippo Cantucci, R. Falcone, C. Castelfranchi","doi":"10.3233/ia-210124","DOIUrl":"https://doi.org/10.3233/ia-210124","url":null,"abstract":"Autonomy is crucial in cooperation. The complexity of HRI scenarios requires autonomous robots able to exploit their superhuman computations (based on DNN, Machine Learning techniques and Big Data) in a trustworthy way. Trustworthiness is not only a matter of accuracy, privacy or security, but it is becoming more and more a matter of adaptation to humans agency. As claimed by Falcone and Castelfranchi, autonomy means the possibility of dislaying or providing an unexpected behavior (including refusal) that departs from a requested (agreed upon or not) behavior. In this sense, the autonomy to decide how to adopt a task delegated by the user, with respect to her/his own real needs and goals, distinguishes intelligent and trustworthy robots from highly performing robots. This kind of smart help can be provided only by cognitive robots able to represent and ascribe mental states (beliefs, goals, intentions, desires etc.) to their interlocutors. The mental states attribution can be the result of complex reasoning mechanisms or can be fast and automatic, based on scripts, roles, categories or stereotypes typically exploited by humans every time they interact in everyday life. In all these cases, robots that build and use cognitive models of humans (that have a Theory of Mind of their interlocutors), have to operate also a meta-evaluation of their own predictive skills to build those models. Robots have to be endowed with the capability to self-trust their skills to interpret the interlocutors and the context, for producing smart and effective decisions towards humans. After exploring the main concepts that make collaboration between humans and robots trustworthy and effective, we present the first of a series of experiments draw for testing different aspects of a designed cognitive architecture for trustworthy HRI. This architecture, based on consolidated theoretical principles (theory of social adjustable autonomy, theory of mind, theory of trust) has the main goal to build cognitive robots that provide smart, trustworthy collaboration, every time a human requires their help. In particular, the experiment has been designed in order to demonstrate how the robot’s capability to learn its own level of self-trust on its predictive abilities in perceiving the user and building a model of her/him, allows it to establish a trustworthy collaboration and to maintain a high level of user’s satisfaction, with respect to the robot’s performance, also when these abilities progressively degrade.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":"16 1","pages":"69-79"},"PeriodicalIF":1.5,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45500346","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}
The movie industry is a highly differentiated context where production studios compete in non-price product attributes, which influences the box office results of a motion picture. Because of the short life cycle and the constant entrance of new competitive products, temporal decisions play a crucial role. Time series of the number of movies on release and the sum of the box office results of the ten top motion pictures (ranked by box office result for that week) present a counterphased seasonality in the US movie market. We suggest that a possible reason is a risk sensitivity adaptation in the behaviour of the movie’s distributors. This paper provides a model supporting this hypothesis. We developed an agent-based model of a movie market, and we simulated it for 15 years. A comparable global behaviour exists when producers schedule the movies according to given risk-sensitive strategies. This research improves the knowledge of the US motion picture market, analyzing a real-world scenario and providing insight into the behaviour of existing firms in a complex environment.
{"title":"Risk sensitive scheduling strategies of production studios on the US movie market: An agent-based simulation","authors":"Francesco Bertolotti, S. Roman","doi":"10.3233/ia-210123","DOIUrl":"https://doi.org/10.3233/ia-210123","url":null,"abstract":"The movie industry is a highly differentiated context where production studios compete in non-price product attributes, which influences the box office results of a motion picture. Because of the short life cycle and the constant entrance of new competitive products, temporal decisions play a crucial role. Time series of the number of movies on release and the sum of the box office results of the ten top motion pictures (ranked by box office result for that week) present a counterphased seasonality in the US movie market. We suggest that a possible reason is a risk sensitivity adaptation in the behaviour of the movie’s distributors. This paper provides a model supporting this hypothesis. We developed an agent-based model of a movie market, and we simulated it for 15 years. A comparable global behaviour exists when producers schedule the movies according to given risk-sensitive strategies. This research improves the knowledge of the US motion picture market, analyzing a real-world scenario and providing insight into the behaviour of existing firms in a complex environment.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":"16 1","pages":"81-92"},"PeriodicalIF":1.5,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48991697","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}
D. Bacciu, Emanuela Girardi, M. Maratea, José Sousa
The COVID-19 pandemic has influenced our lives significantly since March 2020, and a number of initiatives have been put forward in order to tackle its effects, including those focused on technological solutions. In this paper, we present one of such initiatives, i.e. the CLAIRE’s taskforce on AI and COVID-19, in which Artificial Intelligence methodologies and tools are being developed to help the society contrasting the pandemic. We present the different lines of development within the taskforce, some fields in which they are used, and draw few recommendations.
{"title":"Ai & Covid-19","authors":"D. Bacciu, Emanuela Girardi, M. Maratea, José Sousa","doi":"10.3233/ia-210121","DOIUrl":"https://doi.org/10.3233/ia-210121","url":null,"abstract":"The COVID-19 pandemic has influenced our lives significantly since March 2020, and a number of initiatives have been put forward in order to tackle its effects, including those focused on technological solutions. In this paper, we present one of such initiatives, i.e. the CLAIRE’s taskforce on AI and COVID-19, in which Artificial Intelligence methodologies and tools are being developed to help the society contrasting the pandemic. We present the different lines of development within the taskforce, some fields in which they are used, and draw few recommendations.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":"15 1","pages":"45-53"},"PeriodicalIF":1.5,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41660502","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}
Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for automatic assessment of creativity. The proposed solution does not involve human judgement, it is modular and of general applicability. We introduce a new measure, namely DeepCreativity, based on Margaret Boden’s definition of creativity as composed by value, novelty and surprise. We evaluate our methodology (and related measure) considering a case study, i.e., the generation of 19th century American poetry, showing its effectiveness and expressiveness.
{"title":"DeepCreativity: Measuring Creativity with Deep Learning Techniques","authors":"Giorgio Franceschelli, Mirco Musolesi","doi":"10.3233/ia-220136","DOIUrl":"https://doi.org/10.3233/ia-220136","url":null,"abstract":"Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for automatic assessment of creativity. The proposed solution does not involve human judgement, it is modular and of general applicability. We introduce a new measure, namely DeepCreativity, based on Margaret Boden’s definition of creativity as composed by value, novelty and surprise. We evaluate our methodology (and related measure) considering a case study, i.e., the generation of 19th century American poetry, showing its effectiveness and expressiveness.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":"16 1","pages":"151-163"},"PeriodicalIF":1.5,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47171298","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}
The study is devoted to the preservation and reconstruction of the author's choreography at the present stage of development of performing arts. On the example of the creative heritage of the leading Ukrainian choreographer, the founder of the national stage folk dance Vasyl Avramenko, an attempt was made to analyze the peculiarities of modern choreographers' treatment of famous Ukrainian folk stage dances of the first half of the twentieth century.The study found that: in the process of working on folk-stage dance or dance composition, the author of the choreographic text of which is V. Avramenko, it becomes important to focus on artistic qualities that defined V. Avramenko's productions, in particular, emotional tension and the desire to reflect human characters. the context of the historical period, the choreographic poeticization of the figurative side of national patriotism, the use of dramatization as the dominant means, etc .; appeal to V. Avramenko's choreographic heritage of modern choreographers promotes the expression of ethnic identity; the most expedient strategies for processing folk-stage dance or choreographic composition, the author of the choreographic text of which is V. Avramenko, it is expedient to consider the reproduction of the author's production and adaptation to the level of technical training of dancers
{"title":"Vasyl Avramenko's Creative Heritage in Context Folk Choreography of the XXI Century","authors":"Olga Bigus, Kуiv Ukraine Arts","doi":"10.32370/IA_2021_06_9","DOIUrl":"https://doi.org/10.32370/IA_2021_06_9","url":null,"abstract":"The study is devoted to the preservation and reconstruction of the author's choreography at the present stage of development of performing arts. On the example of the creative heritage of the leading Ukrainian choreographer, the founder of the national stage folk dance Vasyl Avramenko, an attempt was made to analyze the peculiarities of modern choreographers' treatment of famous Ukrainian folk stage dances of the first half of the twentieth century.The study found that: in the process of working on folk-stage dance or dance composition, the author of the choreographic text of which is V. Avramenko, it becomes important to focus on artistic qualities that defined V. Avramenko's productions, in particular, emotional tension and the desire to reflect human characters. the context of the historical period, the choreographic poeticization of the figurative side of national patriotism, the use of dramatization as the dominant means, etc .; appeal to V. Avramenko's choreographic heritage of modern choreographers promotes the expression of ethnic identity; the most expedient strategies for processing folk-stage dance or choreographic composition, the author of the choreographic text of which is V. Avramenko, it is expedient to consider the reproduction of the author's production and adaptation to the level of technical training of dancers","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45075296","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}
Nizar Hamadeh, Ali Karouni, Z. Farhat, Hussein El Ghor, Mohamad El Ghor, Israa Katea
Intelligent transport systems have efficiently and effectively proved themselves in settling up the problem of traffic congestion around the world. The multi-agent based transportation system is one of the most important intelligent transport systems, which represents an interaction among the neighbouring vehicles, drivers, roads, infrastructure and vehicles. In this paper, two traffic management models have been created to mitigate congestion and to ensure that emergency vehicles arrive as quickly as possible. A tool-chain SUMO-JADE is employed to create a microscopic simulation symbolizing the interactions of traffic. The simulation model has showed a significant reduction of at least 50% in the average time delay and thus a real improvement in the entire journey time.
{"title":"Intelligent Transportation Systems to Mitigate Road Traffic Congestion","authors":"Nizar Hamadeh, Ali Karouni, Z. Farhat, Hussein El Ghor, Mohamad El Ghor, Israa Katea","doi":"10.3233/ia-200079","DOIUrl":"https://doi.org/10.3233/ia-200079","url":null,"abstract":"Intelligent transport systems have efficiently and effectively proved themselves in settling up the problem of traffic congestion around the world. The multi-agent based transportation system is one of the most important intelligent transport systems, which represents an interaction among the neighbouring vehicles, drivers, roads, infrastructure and vehicles. In this paper, two traffic management models have been created to mitigate congestion and to ensure that emergency vehicles arrive as quickly as possible. A tool-chain SUMO-JADE is employed to create a microscopic simulation symbolizing the interactions of traffic. The simulation model has showed a significant reduction of at least 50% in the average time delay and thus a real improvement in the entire journey time.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":"15 1","pages":"91-104"},"PeriodicalIF":1.5,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45748030","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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