Pub Date : 2013-07-14DOI: 10.1609/aaai.v27i2.18998
Kai Liang, Feng Cao, Zhuofu Bai, Mark Renfrew, M. C. Cavusoglu, Andy Podgurski, Soumya Ray
Adverse and anomalous (A&A) events are a serious concern in medical robots. We describe a system that can rapidly detect such events and predict their occurrence. As part of this system, we describe simulation, data collection and user interface tools we build for a robot for small animal biopsies. The data we collect consists of both the hardware state of the robot and variables in the software controller. We use this data to train dynamic Bayesian network models of the joint hardware-software state-space dynamics of the robot. Our empirical evaluation shows that (i) our models can accurately model normal behavior of the robot, (ii) they can rapidly detect anomalous behavior once it starts, (iii) they can accurately predict a future A&A event within a time window of it starting and (iv) the use of additional software variables beyond the hardware state of the robot is important in being able to detect and predict certain kinds of events.
{"title":"Detection and Prediction of Adverse and Anomalous Events in Medical Robots","authors":"Kai Liang, Feng Cao, Zhuofu Bai, Mark Renfrew, M. C. Cavusoglu, Andy Podgurski, Soumya Ray","doi":"10.1609/aaai.v27i2.18998","DOIUrl":"https://doi.org/10.1609/aaai.v27i2.18998","url":null,"abstract":"Adverse and anomalous (A&A) events are a serious concern in medical robots. We describe a system that can rapidly detect such events and predict their occurrence. As part of this system, we describe simulation, data collection and user interface tools we build for a robot for small animal biopsies. The data we collect consists of both the hardware state of the robot and variables in the software controller. We use this data to train dynamic Bayesian network models of the joint hardware-software state-space dynamics of the robot. Our empirical evaluation shows that (i) our models can accurately model normal behavior of the robot, (ii) they can rapidly detect anomalous behavior once it starts, (iii) they can accurately predict a future A&A event within a time window of it starting and (iv) the use of additional software variables beyond the hardware state of the robot is important in being able to detect and predict certain kinds of events.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117035449","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 : 2013-07-14DOI: 10.1609/aaai.v27i2.19002
Pradeep Varakantham, H. Lau, Z. Yuan
Mass Rapid Transit using rail is a popular mode of transport employed by millions of people in many urban cities across the world. Typically, these networks are massive, used by many and thus, can be a soft target for criminals. In this paper, we consider the problem of scheduling randomised patrols for improving security of such rail networks. Similar to existing work in randomised patrols for protecting critical infrastructure, we also employ Stackelberg Games to represent the problem. In solving the Stackelberg games for massive rail networks, we make two key contributions. Firstly, we provide an approach called RaPtoR for computing randomized strategies in patrol teams, which guarantees (i) Strong Stackelberg equilibrium (SSE); and (ii) Optimality in terms of distance traveled by the patrol teams for specific constraints on schedules. Secondly, we demonstrate RaPtoR on a real world data set corresponding to the rail network in Singapore. Furthermore, we also show that the algorithm scales easily to large rail networks while providing SSE randomized strategies.
{"title":"Scalable Randomized Patrolling for Securing Rapid Transit Networks","authors":"Pradeep Varakantham, H. Lau, Z. Yuan","doi":"10.1609/aaai.v27i2.19002","DOIUrl":"https://doi.org/10.1609/aaai.v27i2.19002","url":null,"abstract":"Mass Rapid Transit using rail is a popular mode of transport employed by millions of people in many urban cities across the world. Typically, these networks are massive, used by many and thus, can be a soft target for criminals. In this paper, we consider the problem of scheduling randomised patrols for improving security of such rail networks. Similar to existing work in randomised patrols for protecting critical infrastructure, we also employ Stackelberg Games to represent the problem. In solving the Stackelberg games for massive rail networks, we make two key contributions. Firstly, we provide an approach called RaPtoR for computing randomized strategies in patrol teams, which guarantees (i) Strong Stackelberg equilibrium (SSE); and (ii) Optimality in terms of distance traveled by the patrol teams for specific constraints on schedules. Secondly, we demonstrate RaPtoR on a real world data set corresponding to the rail network in Singapore. Furthermore, we also show that the algorithm scales easily to large rail networks while providing SSE randomized strategies.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125989825","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 : 2013-07-14DOI: 10.1609/aaai.v27i2.18985
Ulli Waltinger, Dan G. Tecuci, Mihaela Olteanu, Vlad Mocanu, S. Sullivan
This paper describes USI Answers a natural language question answering system for semi-structured industry data. The paper reports on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system’s interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.
{"title":"USI Answers: Natural Language Question Answering Over (Semi-) Structured Industry Data","authors":"Ulli Waltinger, Dan G. Tecuci, Mihaela Olteanu, Vlad Mocanu, S. Sullivan","doi":"10.1609/aaai.v27i2.18985","DOIUrl":"https://doi.org/10.1609/aaai.v27i2.18985","url":null,"abstract":"This paper describes USI Answers a natural language question answering system for semi-structured industry data. The paper reports on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system’s interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128338877","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 : 2013-07-14DOI: 10.1609/aaai.v27i2.18983
Hadrien Cambazard, B. O’Sullivan, H. Simonis
We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland. This system has been deployed since 2010. Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied. Starting from a constraint programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface. Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks. The use of the automated system allowed the dental school to increase student numbers to the maximum possible given the available resources. It also provides the school with a valuable “what-if” analysis tool.
{"title":"The Deployment of a Constraint-Based Dental School Timetabling System","authors":"Hadrien Cambazard, B. O’Sullivan, H. Simonis","doi":"10.1609/aaai.v27i2.18983","DOIUrl":"https://doi.org/10.1609/aaai.v27i2.18983","url":null,"abstract":"We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland. This system has been deployed since 2010. Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied. Starting from a constraint programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface. Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks. The use of the automated system allowed the dental school to increase student numbers to the maximum possible given the available resources. It also provides the school with a valuable “what-if” analysis tool.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127000284","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 : 2013-07-14DOI: 10.1609/aaai.v27i2.18990
M. Buller, Eric Sodomka, W. Tharion, C. Clements, R. Hoyt, O. Jenkins
Emergency workers engaged in strenuous work in hot environments risk overheating and mission failure. We describe a real-time application that would reduce these risks in terms of a real-time thermal-work strain index (SI) estimator; and a Markov Decision Process (MDP) to compute optimal work rate policies. We examined the thermo-physiological responses of 14 experienced U.S. Army Ranger students (26±4 years 1.77±0.04 m; 78.3±7.3 kg) who participated in a strenuous 8 mile time-restricted pass/fail road march conducted under thermally stressful conditions. A thermoregulatory model was used to derive SI state transition probabilities and model the students’ observed and policy driven movement rates. We found that policy end-state SI was significantly lower than SI when modeled using the student’s own movement rates (3.94±0.88 vs. 5.62±1.20, P<0.001). We also found an inverse relationship between our policy impact and maximum SI (r=0.64 P<0.05). These results suggest that modeling real world missions as an MDP can provide optimal work rate policies that improve thermal safety and allow students to finish in a “fresher” state. Ultimately, SI state estimation and MDP models incorporated into wearable physiological monitoring systems could provide real-time work rate guidance, thus minimizing thermal work-strain while maximizing the likelihood of accomplishing mission tasks.
{"title":"Policies to Optimize Work Performance and Thermal Safety in Exercising Humans","authors":"M. Buller, Eric Sodomka, W. Tharion, C. Clements, R. Hoyt, O. Jenkins","doi":"10.1609/aaai.v27i2.18990","DOIUrl":"https://doi.org/10.1609/aaai.v27i2.18990","url":null,"abstract":"\u0000 \u0000 \u0000Emergency workers engaged in strenuous work in hot environments risk overheating and mission failure. We describe a real-time application that would reduce these risks in terms of a real-time thermal-work strain index (SI) estimator; and a Markov Decision Process (MDP) to compute optimal work rate policies. We examined the thermo-physiological responses of 14 experienced U.S. Army Ranger students (26±4 years 1.77±0.04 m; 78.3±7.3 kg) who participated in a strenuous 8 mile time-restricted pass/fail road march conducted under thermally stressful conditions. A thermoregulatory model was used to derive SI state transition probabilities and model the students’ observed and policy driven movement rates. We found that policy end-state SI was significantly lower than SI when modeled using the student’s own movement rates (3.94±0.88 vs. 5.62±1.20, P<0.001). We also found an inverse relationship between our policy impact and maximum SI (r=0.64 P<0.05). These results suggest that modeling real world missions as an MDP can provide optimal work rate policies that improve thermal safety and allow students to finish in a “fresher” state. Ultimately, SI state estimation and MDP models incorporated into wearable physiological monitoring systems could provide real-time work rate guidance, thus minimizing thermal work-strain while maximizing the likelihood of accomplishing mission tasks. \u0000 \u0000 \u0000","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129046962","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 : 2013-07-14DOI: 10.1609/aaai.v27i2.19001
Sriraam Natarajan, K. Kersting, E. Ip, D. Jacobs, J. Carr
Coronary heart disease (CHD) is a major cause of death worldwide. In the U.S. CHD is responsible for approximated 1 in every 6 deaths with a coronary event occurring every 25 seconds and about 1 death every minute based on data current to 2007. Although a multitude of cardiovascular risks factors have been identified, CHD actually reflects complex interactions of these factors over time. Today’s datasets from longitudinal studies offer great promise to uncover these interactions but also pose enormous analytical problems due to typically large amount of both discrete and continuous measurements and risk factors with potential long-range interactions over time. Our investigation demonstrates that a statistical relational analysis of longitudinal data can easily uncover complex interactions of risks factors and actually predict future coronary artery calcification (CAC) levels — an indicator of the risk of CHD present subclinically in an individual — significantly better than traditional non-relational approaches. The uncovered long-range interactions between risk factors conform to existing clinical knowledge and are successful in identifying risk factors at the early adult stage. This may contribute to monitoring young adults via smartphones and to designing patient-specific treatments in young adults to mitigate their risk later.
{"title":"Early Prediction of Coronary Artery Calcification Levels Using Machine Learning","authors":"Sriraam Natarajan, K. Kersting, E. Ip, D. Jacobs, J. Carr","doi":"10.1609/aaai.v27i2.19001","DOIUrl":"https://doi.org/10.1609/aaai.v27i2.19001","url":null,"abstract":"Coronary heart disease (CHD) is a major cause of death worldwide. In the U.S. CHD is responsible for approximated 1 in every 6 deaths with a coronary event occurring every 25 seconds and about 1 death every minute based on data current to 2007. Although a multitude of cardiovascular risks factors have been identified, CHD actually reflects complex interactions of these factors over time. Today’s datasets from longitudinal studies offer great promise to uncover these interactions but also pose enormous analytical problems due to typically large amount of both discrete and continuous measurements and risk factors with potential long-range interactions over time. Our investigation demonstrates that a statistical relational analysis of longitudinal data can easily uncover complex interactions of risks factors and actually predict future coronary artery calcification (CAC) levels — an indicator of the risk of CHD present subclinically in an individual — significantly better than traditional non-relational approaches. The uncovered long-range interactions between risk factors conform to existing clinical knowledge and are successful in identifying risk factors at the early adult stage. This may contribute to monitoring young adults via smartphones and to designing patient-specific treatments in young adults to mitigate their risk later.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124109967","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 : 2012-07-22DOI: 10.1609/aaai.v26i2.18966
Stephanie Valentine, Francisco Vides, G. Lucchese, D. Turner, Hong-hoe Kim, Wenzhe Li, J. Linsey, T. Hammond
Introductory engineering courses within large universities often have annual enrollments which can reach up to a thousand students. It is very challenging to achieve differentiated instruction in classrooms with class sizes and student diversity of such great magnitude. Professors can only assess whether students have mastered a concept by using multiple choice questions, while detailed homework assignments, such as planar truss diagrams, are rarely assigned because professors and teaching assistants would be too overburdened with grading to return assignments with valuable feedback in a timely manner. In this paper, we introduce Mechanix, a sketch-based deployed tutoring system for engineering students enrolled in statics courses. Our system not only allows students to enter planar truss and free body diagrams into the system just as they would with pencil and paper, but our system checks the student’s work against a hand-drawn answer entered by the instructor, and then returns immediate and detailed feedback to the student. Students are allowed to correct any errors in their work and resubmit until the entire content is correct and thus all of the objectives are learned. Since Mechanix facilitates the grading and feedback processes, instructors are now able to assign free response questions, increasing teacher’s knowledge of student comprehension. Furthermore, the iterative correction process allows students to learn during a test, rather than simply displaying memorized information.
{"title":"Mechanix: A Sketch-Based Tutoring System for Statics Courses","authors":"Stephanie Valentine, Francisco Vides, G. Lucchese, D. Turner, Hong-hoe Kim, Wenzhe Li, J. Linsey, T. Hammond","doi":"10.1609/aaai.v26i2.18966","DOIUrl":"https://doi.org/10.1609/aaai.v26i2.18966","url":null,"abstract":"Introductory engineering courses within large universities often have annual enrollments which can reach up to a thousand students. It is very challenging to achieve differentiated instruction in classrooms with class sizes and student diversity of such great magnitude. Professors can only assess whether students have mastered a concept by using multiple choice questions, while detailed homework assignments, such as planar truss diagrams, are rarely assigned because professors and teaching assistants would be too overburdened with grading to return assignments with valuable feedback in a timely manner. In this paper, we introduce Mechanix, a sketch-based deployed tutoring system for engineering students enrolled in statics courses. Our system not only allows students to enter planar truss and free body diagrams into the system just as they would with pencil and paper, but our system checks the student’s work against a hand-drawn answer entered by the instructor, and then returns immediate and detailed feedback to the student. Students are allowed to correct any errors in their work and resubmit until the entire content is correct and thus all of the objectives are learned. Since Mechanix facilitates the grading and feedback processes, instructors are now able to assign free response questions, increasing teacher’s knowledge of student comprehension. Furthermore, the iterative correction process allows students to learn during a test, rather than simply displaying memorized information.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116751800","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 : 2012-07-22DOI: 10.1609/aaai.v26i2.18978
D. Sislák, P. Volf, M. Pechoucek, Christopher T. Cannon, Duc N. Nguyen, W. Regli
The Next-Generation Transportation program coordinates the evolution and transformation of the current air-traffic management (ATM) system for the National Airspace System (NAS). Currently the NAS has a limited capacity and cannot handle the increasing future air traffic demands. However, before newly proposed ATM concepts are deployed they must be rigorously evaluated under realistic conditions. This paper presents AGENTFLY, an emerging NAS-wide highfidelity multi-agent ATM simulator with precise emulation of the human controller operation workload model and human-system interaction. The simulator is validated using a flight scenario developed by the U.S. Federal Aviation Administration that is based on real data. We present preliminary results focusing on the accuracy of the simulated controllers within AGENTFLY.
{"title":"Multi-Agent Simulation of En-Route Human Air-Traffic Controller","authors":"D. Sislák, P. Volf, M. Pechoucek, Christopher T. Cannon, Duc N. Nguyen, W. Regli","doi":"10.1609/aaai.v26i2.18978","DOIUrl":"https://doi.org/10.1609/aaai.v26i2.18978","url":null,"abstract":"The Next-Generation Transportation program coordinates the evolution and transformation of the current air-traffic management (ATM) system for the National Airspace System (NAS). Currently the NAS has a limited capacity and cannot handle the increasing future air traffic demands. However, before newly proposed ATM concepts are deployed they must be rigorously evaluated under realistic conditions. This paper presents AGENTFLY, an emerging NAS-wide highfidelity multi-agent ATM simulator with precise emulation of the human controller operation workload model and human-system interaction. The simulator is validated using a flight scenario developed by the U.S. Federal Aviation Administration that is based on real data. We present preliminary results focusing on the accuracy of the simulated controllers within AGENTFLY.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127172615","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 : 2012-07-22DOI: 10.1609/aaai.v26i2.18975
Zhengyu Yin, A. Jiang, Milind Tambe, Christopher Kiekintveld, Kevin Leyton-Brown, T. Sandholm, John P. Sullivan
In proof-of-payment transit systems, passengers are legally required to purchase tickets before entering but are not physically forced to do so. Instead, patrol units move about the transit system, inspecting the tickets of passengers, who face fines if caught fare evading. The deterrence of such fines depends on the unpredictability and effectiveness of the patrols. In this paper, we present TRUSTS, an application for scheduling randomized patrols for fare inspection in transit systems. TRUSTS models the problem of computing patrol strategies as a leader-follower Stackelberg game where the objective is to deter fare evasion and hence maximize revenue. This problem differs from previously studied Stackelberg settings in that the leader strategies must satisfy massive temporal and spatial constraints; moreover, unlike in these counterterrorism-motivated Stackelberg applications, a large fraction of the ridership might realistically consider fare evasion, and so the number of followers is potentially huge. A third key novelty in our work is deliberate simplification of leader strategies to make patrols easier to be executed. We present an efficient algorithm for computing such patrol strategies and present experimental results using real-world ridership data from the Los Angeles Metro Rail system. The Los Angeles County Sheriff’s department has begun trials of TRUSTS.
{"title":"TRUSTS: Scheduling Randomized Patrols for Fare Inspection in Transit Systems","authors":"Zhengyu Yin, A. Jiang, Milind Tambe, Christopher Kiekintveld, Kevin Leyton-Brown, T. Sandholm, John P. Sullivan","doi":"10.1609/aaai.v26i2.18975","DOIUrl":"https://doi.org/10.1609/aaai.v26i2.18975","url":null,"abstract":"In proof-of-payment transit systems, passengers are legally required to purchase tickets before entering but are not physically forced to do so. Instead, patrol units move about the transit system, inspecting the tickets of passengers, who face fines if caught fare evading. The deterrence of such fines depends on the unpredictability and effectiveness of the patrols. In this paper, we present TRUSTS, an application for scheduling randomized patrols for fare inspection in transit systems. TRUSTS models the problem of computing patrol strategies as a leader-follower Stackelberg game where the objective is to deter fare evasion and hence maximize revenue. This problem differs from previously studied Stackelberg settings in that the leader strategies must satisfy massive temporal and spatial constraints; moreover, unlike in these counterterrorism-motivated Stackelberg applications, a large fraction of the ridership might realistically consider fare evasion, and so the number of followers is potentially huge. A third key novelty in our work is deliberate simplification of leader strategies to make patrols easier to be executed. We present an efficient algorithm for computing such patrol strategies and present experimental results using real-world ridership data from the Los Angeles Metro Rail system. The Los Angeles County Sheriff’s department has begun trials of TRUSTS.","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129689400","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 : 2012-07-22DOI: 10.1609/aaai.v26i2.18971
J. Folsom-Kovarik, G. Sukthankar, S. Schatz
This paper describes the development and empirical testing of an intelligent tutoring system (ITS) with two emerging methodologies: (1) a partially observable Markov decision process (POMDP) for representing the learner model and (2) inquiry modeling, which informs the learner model with questions learners ask during instruction. POMDPs have been successfully applied to non-ITS domains but, until recently, have seemed intractable for large-scale intelligent tutoring challenges. New, ITS-specific representations leverage common regularities in intelligent tutoring to make a POMDP practical as a learner model. Inquiry modeling is a novel paradigm for informing learner models by observing rich features of learners’ help requests such as categorical content, context, and timing. The experiment described in this paper demonstrates that inquiry modeling and planning with POMDPs can yield significant and substantive learning improvements in a realistic, scenario-based training task.
{"title":"Integrating Learner Help Requests Using a POMDP in an Adaptive Training System","authors":"J. Folsom-Kovarik, G. Sukthankar, S. Schatz","doi":"10.1609/aaai.v26i2.18971","DOIUrl":"https://doi.org/10.1609/aaai.v26i2.18971","url":null,"abstract":"\u0000 \u0000 \u0000This paper describes the development and empirical testing of an intelligent tutoring system (ITS) with two emerging methodologies: (1) a partially observable Markov decision process (POMDP) for representing the learner model and (2) inquiry modeling, which informs the learner model with questions learners ask during instruction. POMDPs have been successfully applied to non-ITS domains but, until recently, have seemed intractable for large-scale intelligent tutoring challenges. New, ITS-specific representations leverage common regularities in intelligent tutoring to make a POMDP practical as a learner model. Inquiry modeling is a novel paradigm for informing learner models by observing rich features of learners’ help requests such as categorical content, context, and timing. The experiment described in this paper demonstrates that inquiry modeling and planning with POMDPs can yield significant and substantive learning improvements in a realistic, scenario-based training task. \u0000 \u0000 \u0000","PeriodicalId":408078,"journal":{"name":"Conference on Innovative Applications of Artificial Intelligence","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114976925","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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