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class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101274682/International_Journal_of_Computer_Science_and_Application_Vol_4_No_1_April_2015_1_2324_7037_15_01_001_08_2015_DEStech_Publications_Inc_doi_10_12783_ijcsa_2015_0401_01_Feature_Selection_Algorithm_Used_to_Classify_Faults_in_Turbine_Bearings"><img alt="Research paper thumbnail of International Journal of Computer Science and Application , Vol. 4, No. 1—April 2015 1 2324 ‐ 7037/15/01 001 ‐ 08 © 2015 DEStech Publications, Inc. doi: 10.12783/ijcsa.2015.0401.01 Feature Selection Algorithm Used to Classify Faults in Turbine Bearings" class="work-thumbnail" src="https://attachments.academia-assets.com/101861009/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101274682/International_Journal_of_Computer_Science_and_Application_Vol_4_No_1_April_2015_1_2324_7037_15_01_001_08_2015_DEStech_Publications_Inc_doi_10_12783_ijcsa_2015_0401_01_Feature_Selection_Algorithm_Used_to_Classify_Faults_in_Turbine_Bearings">International Journal of Computer Science and Application , Vol. 4, No. 1—April 2015 1 2324 ‐ 7037/15/01 001 ‐ 08 © 2015 DEStech Publications, Inc. doi: 10.12783/ijcsa.2015.0401.01 Feature Selection Algorithm Used to Classify Faults in Turbine Bearings</a></div><div class="wp-workCard_item"><span>International Journal of Computer Science and Application</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0d5ff67529f148cad23a822b6cc52d11" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101274682]").text(description); $(".js-view-count[data-work-id=101274682]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101274682; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101274682']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 101274682, container: "", }); });</script></span></div><div 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="96531582"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/96531582/Driver_Behaviour_Profiling_Using_Dynamic_Bayesian_Network"><img alt="Research paper thumbnail of Driver Behaviour Profiling Using Dynamic Bayesian Network" class="work-thumbnail" src="https://attachments.academia-assets.com/98404654/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/96531582/Driver_Behaviour_Profiling_Using_Dynamic_Bayesian_Network">Driver Behaviour Profiling Using Dynamic Bayesian Network</a></div><div class="wp-workCard_item"><span>International Journal of Modern Education and Computer Science</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6b30f5c37b311a1756279451e3b4c8f0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{&quot;attachment_id&quot;:98404654,&quot;asset_id&quot;:96531582,&quot;asset_type&quot;:&quot;Work&quot;,&quot;button_location&quot;:&quot;profile&quot;}" href="https://www.academia.edu/attachments/98404654/download_file?st=MTczMjc4ODU3MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="96531582"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="96531582"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 96531582; 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The biggest challenge now lies on how to monitor and analyse behaviours of vehicle drivers as a catalyst to road safety. Driver behaviour depends on the state and nature of the road, the state of the driver, vehicle conditions, and actions of other road users among other factors. This paper illustrates the ability of Dynamic Bayesian Networks towards determination of driving styles with respect to acceleration, cornering and braking patterns. Bayesian Networks are probabilistic graphical models that map a set of variables and their conditional dependencies. Sample test results showed that the 2-Time-slice Bayesian Network model is suitable for generation of driver profiles using only four GPS data parameters namely speed, altitude, direction and signal strength against time. The model classifies driver profiles into two sets of observations: driver behaviour and nature of operational environment. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91279132"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91279132/MAS_Architectural_Model_for_Dialog_Systems_with_Advancing_Conversations"><img alt="Research paper thumbnail of MAS Architectural Model for Dialog Systems with Advancing Conversations" class="work-thumbnail" src="https://attachments.academia-assets.com/94610745/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91279132/MAS_Architectural_Model_for_Dialog_Systems_with_Advancing_Conversations">MAS Architectural Model for Dialog Systems with Advancing Conversations</a></div><div class="wp-workCard_item"><span>International Journal of Scientific Research in Computer Science, Engineering and Information Technology</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recent handcrafts on dialog manager in task-oriented dialog systems (TODS) offer great promises o...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Recent handcrafts on dialog manager in task-oriented dialog systems (TODS) offer great promises on handling conversations. However, most tend to be shortsighted in handling advancing conversations. Modelling the future direction on conversations is crucial for TODS that can be scaled across multi-domain. This paper proposes a novel architectural model for the dialog manager, (MAS_DM). In this model, the dialog manager is a MAS. The architecture consists of multiple intelligent interacting agents, namely, state agent, master agent, and dialog agents. Each agent performs a set of tasks to achieve the overall goal of advancing the conversation within a topic. In this paper, the particular component of the Dialogue Manager, and Strategy selection has been discussed in detail. The notion of learning is essential, since it is intended to provide a means to which the agents will adapt to their environment. We show how to combine MAS and RL to enable agents learn a topic of interest and sup...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="57546eb86700160c1dcec77642429c62" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{&quot;attachment_id&quot;:94610745,&quot;asset_id&quot;:91279132,&quot;asset_type&quot;:&quot;Work&quot;,&quot;button_location&quot;:&quot;profile&quot;}" href="https://www.academia.edu/attachments/94610745/download_file?st=MTczMjc4ODU3MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91279132"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91279132"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91279132; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91279132]").text(description); $(".js-view-count[data-work-id=91279132]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91279132; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91279132']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91279132, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "57546eb86700160c1dcec77642429c62" } } $('.js-work-strip[data-work-id=91279132]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91279132,"title":"MAS Architectural Model for Dialog Systems with Advancing Conversations","translated_title":"","metadata":{"abstract":"Recent handcrafts on dialog manager in task-oriented dialog systems (TODS) offer great promises on handling conversations. However, most tend to be shortsighted in handling advancing conversations. Modelling the future direction on conversations is crucial for TODS that can be scaled across multi-domain. This paper proposes a novel architectural model for the dialog manager, (MAS_DM). In this model, the dialog manager is a MAS. The architecture consists of multiple intelligent interacting agents, namely, state agent, master agent, and dialog agents. Each agent performs a set of tasks to achieve the overall goal of advancing the conversation within a topic. In this paper, the particular component of the Dialogue Manager, and Strategy selection has been discussed in detail. The notion of learning is essential, since it is intended to provide a means to which the agents will adapt to their environment. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="16444778" id="papers"><div class="js-work-strip profile--work_container" data-work-id="101274682"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101274682/International_Journal_of_Computer_Science_and_Application_Vol_4_No_1_April_2015_1_2324_7037_15_01_001_08_2015_DEStech_Publications_Inc_doi_10_12783_ijcsa_2015_0401_01_Feature_Selection_Algorithm_Used_to_Classify_Faults_in_Turbine_Bearings"><img alt="Research paper thumbnail of International Journal of Computer Science and Application , Vol. 4, No. 1—April 2015 1 2324 ‐ 7037/15/01 001 ‐ 08 © 2015 DEStech Publications, Inc. doi: 10.12783/ijcsa.2015.0401.01 Feature Selection Algorithm Used to Classify Faults in Turbine Bearings" class="work-thumbnail" src="https://attachments.academia-assets.com/101861009/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101274682/International_Journal_of_Computer_Science_and_Application_Vol_4_No_1_April_2015_1_2324_7037_15_01_001_08_2015_DEStech_Publications_Inc_doi_10_12783_ijcsa_2015_0401_01_Feature_Selection_Algorithm_Used_to_Classify_Faults_in_Turbine_Bearings">International Journal of Computer Science and Application , Vol. 4, No. 1—April 2015 1 2324 ‐ 7037/15/01 001 ‐ 08 © 2015 DEStech Publications, Inc. doi: 10.12783/ijcsa.2015.0401.01 Feature Selection Algorithm Used to Classify Faults in Turbine Bearings</a></div><div class="wp-workCard_item"><span>International Journal of Computer Science and Application</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0d5ff67529f148cad23a822b6cc52d11" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{&quot;attachment_id&quot;:101861009,&quot;asset_id&quot;:101274682,&quot;asset_type&quot;:&quot;Work&quot;,&quot;button_location&quot;:&quot;profile&quot;}" href="https://www.academia.edu/attachments/101861009/download_file?st=MTczMjc4ODU3MSw4LjIyMi4yMDguMTQ2&st=MTczMjc4ODU3MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101274682"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101274682"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101274682; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101274682]").text(description); $(".js-view-count[data-work-id=101274682]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101274682; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101274682']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 101274682, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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The biggest challenge now lies on how to monitor and analyse behaviours of vehicle drivers as a catalyst to road safety. Driver behaviour depends on the state and nature of the road, the state of the driver, vehicle conditions, and actions of other road users among other factors. This paper illustrates the ability of Dynamic Bayesian Networks towards determination of driving styles with respect to acceleration, cornering and braking patterns. Bayesian Networks are probabilistic graphical models that map a set of variables and their conditional dependencies. Sample test results showed that the 2-Time-slice Bayesian Network model is suitable for generation of driver profiles using only four GPS data parameters namely speed, altitude, direction and signal strength against time. The model classifies driver profiles into two sets of observations: driver behaviour and nature of operational environment. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91279132"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91279132/MAS_Architectural_Model_for_Dialog_Systems_with_Advancing_Conversations"><img alt="Research paper thumbnail of MAS Architectural Model for Dialog Systems with Advancing Conversations" class="work-thumbnail" src="https://attachments.academia-assets.com/94610745/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91279132/MAS_Architectural_Model_for_Dialog_Systems_with_Advancing_Conversations">MAS Architectural Model for Dialog Systems with Advancing Conversations</a></div><div class="wp-workCard_item"><span>International Journal of Scientific Research in Computer Science, Engineering and Information Technology</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recent handcrafts on dialog manager in task-oriented dialog systems (TODS) offer great promises o...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Recent handcrafts on dialog manager in task-oriented dialog systems (TODS) offer great promises on handling conversations. However, most tend to be shortsighted in handling advancing conversations. Modelling the future direction on conversations is crucial for TODS that can be scaled across multi-domain. This paper proposes a novel architectural model for the dialog manager, (MAS_DM). In this model, the dialog manager is a MAS. The architecture consists of multiple intelligent interacting agents, namely, state agent, master agent, and dialog agents. Each agent performs a set of tasks to achieve the overall goal of advancing the conversation within a topic. In this paper, the particular component of the Dialogue Manager, and Strategy selection has been discussed in detail. The notion of learning is essential, since it is intended to provide a means to which the agents will adapt to their environment. We show how to combine MAS and RL to enable agents learn a topic of interest and sup...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="57546eb86700160c1dcec77642429c62" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{&quot;attachment_id&quot;:94610745,&quot;asset_id&quot;:91279132,&quot;asset_type&quot;:&quot;Work&quot;,&quot;button_location&quot;:&quot;profile&quot;}" href="https://www.academia.edu/attachments/94610745/download_file?st=MTczMjc4ODU3MSw4LjIyMi4yMDguMTQ2&st=MTczMjc4ODU3MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91279132"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91279132"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91279132; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91279132]").text(description); $(".js-view-count[data-work-id=91279132]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91279132; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91279132']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91279132, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "57546eb86700160c1dcec77642429c62" } } $('.js-work-strip[data-work-id=91279132]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91279132,"title":"MAS Architectural Model for Dialog Systems with Advancing Conversations","translated_title":"","metadata":{"abstract":"Recent handcrafts on dialog manager in task-oriented dialog systems (TODS) offer great promises on handling conversations. However, most tend to be shortsighted in handling advancing conversations. Modelling the future direction on conversations is crucial for TODS that can be scaled across multi-domain. This paper proposes a novel architectural model for the dialog manager, (MAS_DM). In this model, the dialog manager is a MAS. The architecture consists of multiple intelligent interacting agents, namely, state agent, master agent, and dialog agents. Each agent performs a set of tasks to achieve the overall goal of advancing the conversation within a topic. In this paper, the particular component of the Dialogue Manager, and Strategy selection has been discussed in detail. The notion of learning is essential, since it is intended to provide a means to which the agents will adapt to their environment. We show how to combine MAS and RL to enable agents learn a topic of interest and sup...","publisher":"Technoscience Academy","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology"},"translated_abstract":"Recent handcrafts on dialog manager in task-oriented dialog systems (TODS) offer great promises on handling conversations. However, most tend to be shortsighted in handling advancing conversations. Modelling the future direction on conversations is crucial for TODS that can be scaled across multi-domain. This paper proposes a novel architectural model for the dialog manager, (MAS_DM). In this model, the dialog manager is a MAS. The architecture consists of multiple intelligent interacting agents, namely, state agent, master agent, and dialog agents. Each agent performs a set of tasks to achieve the overall goal of advancing the conversation within a topic. In this paper, the particular component of the Dialogue Manager, and Strategy selection has been discussed in detail. The notion of learning is essential, since it is intended to provide a means to which the agents will adapt to their environment. 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