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Artificial Intelligence and Robotics
Research Collection School Of Computing and Information Systems
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Articles 31 - 43 of 43
Full-Text Articles in Physical Sciences and Mathematics
Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau
Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We study a real-world problem arising from the operations of a hospital service provider, which we term the master physician scheduling problem. It is a planning problem of assigning physicians’ full range of day-to-day duties (including surgery, clinics, scopes, calls, administration) to the defined time slots/shifts over a time horizon, incorporating a large number of constraints and complex physician preferences. The goals are to satisfy as many physicians’ preferences and duty requirements as possible while ensuring optimum usage of available resources. We propose mathematical programming models that represent different variants of this problem. The models were tested on a real …
Implementation Of Slowly Changing Dimension To Data Warehouse To Manage Marketing Campaigns In Banks, Lihui Wang, Junyu Choy, Michelle L. F. Cheong
Implementation Of Slowly Changing Dimension To Data Warehouse To Manage Marketing Campaigns In Banks, Lihui Wang, Junyu Choy, Michelle L. F. Cheong
Research Collection School Of Computing and Information Systems
Management of updating and recording campaign leads in data warehouse of any banking environment is complex especially with multiple campaigns are active simultaneously. As a way to avoid overly contacting customers for sales-based marketing contacts, the concept of Recency Frame is introduced to “lock” the customers who are targeted in Sales-based campaign for a specified time period. During this Recency Frame, the customer cannot be targeted by other Sales-based campaign under the same channel. This approach increased the difficulties of managing the customers’ data with proper data updating and storing and procedures have to be placed and made sufficiently robust …
Disclosing Climate Change Patterns Using An Adaptive Markov Chain Pattern Detection Method, Zhaoxia Wang, Gary Lee, Hoong Maeng Chan, Reuben Li, Xiuju Fu, Rick Goh, Pauline A. W. Poh Kim, Martin L. Hibberd, Hoong Chor Chin
Disclosing Climate Change Patterns Using An Adaptive Markov Chain Pattern Detection Method, Zhaoxia Wang, Gary Lee, Hoong Maeng Chan, Reuben Li, Xiuju Fu, Rick Goh, Pauline A. W. Poh Kim, Martin L. Hibberd, Hoong Chor Chin
Research Collection School Of Computing and Information Systems
This paper proposes an adaptive Markov chain pattern detection (AMCPD) method for disclosing the climate change patterns of Singapore through meteorological data mining. Meteorological variables, including daily mean temperature, mean dew point temperature, mean visibility, mean wind speed, maximum sustained wind speed, maximum temperature and minimum temperature are simultaneously considered for identifying climate change patterns in this study. The results depict various weather patterns from 1962 to 2011 in Singapore, based on the records of the Changi Meteorological Station. Different scenarios with varied cluster thresholds are employed for testing the sensitivity of the proposed method. The robustness of the proposed …
Enabling Generative, Emergent Artificial Culture, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann
Enabling Generative, Emergent Artificial Culture, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann
Research Collection School Of Computing and Information Systems
Despite the demand for culturally placed agent models, an adequate simulation approach to the relationship between group-cultural and individual-psychological qualities, including culture emergence, is just appearing. It could be argued that we are at the beginning of a domain forming process, a dawn of generative, emergent artificial culture. In this context we discuss current limitations and argue e.g. that too far reaching agent simplicity within Agent Based Modeling limits the emergence of realistic cultural-conventional level and we advocate psychologically rich models of culture forming mechanisms. We propose an approach to cultural phenomena modeling based on the interaction of habitual, affective …
Tesla: An Energy-Saving Agent That Leverages Schedule Flexibility, Jun Young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Burcin Becerik-Gerber, Milind Tambe
Tesla: An Energy-Saving Agent That Leverages Schedule Flexibility, Jun Young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Burcin Becerik-Gerber, Milind Tambe
Research Collection School Of Computing and Information Systems
This innovative application paper presents TESLA, an agent-based application for optimizing the energy use in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. TESLA provides three key contributions: (i) three online scheduling algorithms that consider flexibility of people’s preferences for energyefficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; and (iii) surveys of real users that indicate that TESLA’s assumptions exist in practice. TESLA was evaluated on data of over 110,000 meetings held …
Delayed Insertion And Rule Effect Moderation Of Domain Knowledge For Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan
Delayed Insertion And Rule Effect Moderation Of Domain Knowledge For Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Though not a fundamental pre-requisite to efficient machine learning, insertion of domain knowledge into adaptive virtual agent is nonetheless known to improve learning efficiency and reduce model complexity. Conventionally, domain knowledge is inserted prior to learning. Despite being effective, such approach may not always be feasible. Firstly, the effect of domain knowledge is assumed and can be inaccurate. Also, domain knowledge may not be available prior to learning. In addition, the insertion of domain knowledge can frame learning and hamper the discovery of more effective knowledge. Therefore, this work advances the use of domain knowledge by proposing to delay the …
Decision Support For Assorted Populations In Uncertain And Congested Environments, Pradeep Reddy Varakantham, Asrar Ahmed, Shih-Fen Cheng
Decision Support For Assorted Populations In Uncertain And Congested Environments, Pradeep Reddy Varakantham, Asrar Ahmed, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
This research is motivated by large scale problems in urban transportation and labor mobility where there is congestion for resources and uncertainty in movement. In such domains, even though the individual agents do not have an identity of their own and do not explicitly interact with other agents, they effect other agents. While there has been much research in handling such implicit effects, it has primarily assumed deterministic movements of agents. We address the issue of decision support for individual agents that are identical and have involuntary movements in dynamic environments. For instance, in a taxi fleet serving a city, …
Automated Parameter Tuning Framework For Heterogeneous And Large Instances: Case Study In Quadratic Assignment Problem, Linda Lindawati, Zhi Yuan, Hoong Chuin Lau, Feida Zhu
Automated Parameter Tuning Framework For Heterogeneous And Large Instances: Case Study In Quadratic Assignment Problem, Linda Lindawati, Zhi Yuan, Hoong Chuin Lau, Feida Zhu
Research Collection School Of Computing and Information Systems
This paper is concerned with automated tuning of parameters of algorithms to handle heterogeneous and large instances. We propose an automated parameter tuning framework with the capability to provide instance-specific parameter configurations. We report preliminary results on the Quadratic Assignment Problem (QAP) and show that our framework provides a significant improvement on solutions qualities with much smaller tuning computational time.
Moving Object Detection With Laser Scanners, Christoph Mertz, Luis E. Navarro-Serment, Robert Maclachlan, Paul Rybski, Aaron Steinfeld, Arne Suppe, Christopher Urmson, Nicolas Vandapel, Martial Hebert, Chuck Thorpe, David Duggins, Jay Gowdy
Moving Object Detection With Laser Scanners, Christoph Mertz, Luis E. Navarro-Serment, Robert Maclachlan, Paul Rybski, Aaron Steinfeld, Arne Suppe, Christopher Urmson, Nicolas Vandapel, Martial Hebert, Chuck Thorpe, David Duggins, Jay Gowdy
Research Collection School Of Computing and Information Systems
The detection and tracking of moving objects is an essential task in robotics. The CMU-RI Navlab group has developed such a system that uses a laser scanner as its primary sensor. We will describe our algorithm and its use in several applications. Our system worked successfully on indoor and outdoor platforms and with several different kinds and configurations of two-dimensional and three-dimensional laser scanners. The applications vary from collision warning systems, people classification, observing human tracks, and input to a dynamic planner. Several of these systems were evaluated in live field tests and shown to be robust and reliable. (C) …
Regret Based Robust Solutions For Uncertain Markov Decision Processes, Asrar Ahmed, Pradeep Reddy Varakantham, Yossiri Adulyasak, Patrick Jaillet
Regret Based Robust Solutions For Uncertain Markov Decision Processes, Asrar Ahmed, Pradeep Reddy Varakantham, Yossiri Adulyasak, Patrick Jaillet
Research Collection School Of Computing and Information Systems
In this paper, we seek robust policies for uncertain Markov Decision Processes (MDPs). Most robust optimization approaches for these problems have focussed on the computation of maximin policies which maximize the value corresponding to the worst realization of the uncertainty. Recent work has proposed minimax regret as a suitable alternative to the maximin objective for robust optimization. However, existing algorithms for handling minimax regret are restricted to models with uncertainty over rewards only. We provide algorithms that employ sampling to improve across multiple dimensions: (a) Handle uncertainties over both transition and reward models; (b) Dependence of model uncertainties across state, …
Clustering Of Search Trajectory And Its Application To Parameter Tuning, Linda Lindawati, Hoong Chuin Lau, David Lo
Clustering Of Search Trajectory And Its Application To Parameter Tuning, Linda Lindawati, Hoong Chuin Lau, David Lo
Research Collection School Of Computing and Information Systems
This paper is concerned with automated classification of Combinatorial Optimization Problem instances for instance-specific parameter tuning purpose. We propose the CluPaTra Framework, a generic approach to CLUster instances based on similar PAtterns according to search TRAjectories and apply it on parameter tuning. The key idea is to use the search trajectory as a generic feature for clustering problem instances. The advantage of using search trajectory is that it can be obtained from any local-search based algorithm with small additional computation time. We explore and compare two different search trajectory representations, two sequence alignment techniques (to calculate similarities) as well as …
Sensor Feature Selection And Combination For Stress Identification Using Combinatorial Fusion, Yong Deng, Zhonghai Wu, Chao-Hsien Chu, Qixun Zhang, D. Frank Hsu
Sensor Feature Selection And Combination For Stress Identification Using Combinatorial Fusion, Yong Deng, Zhonghai Wu, Chao-Hsien Chu, Qixun Zhang, D. Frank Hsu
Research Collection School Of Computing and Information Systems
The identification of stressfulness under certain driving condition is an important issue for safety, security and health. Sensors and systems have been placed or implemented as wearable devices for drivers. Features are extracted from the data collected and combined to predict symptoms. The challenge is to select the feature set most relevant for stress. In this paper, we propose a feature selection method based on the performance and the diversity between two features. The feature sets selected are then combined using a combinatorial fusion. We also compare our results with other combination methods such as naïve Bayes, support vector machine, …
Uncertain Congestion Games With Assorted Human Agent Populations , Pradeep Reddy Varakantham, Asrar Ahmed, Shih-Fen Cheng
Uncertain Congestion Games With Assorted Human Agent Populations , Pradeep Reddy Varakantham, Asrar Ahmed, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Congestion games model a wide variety of real-world resource congestion problems, such as selfish network routing, traffic route guidance in congested areas, taxi fleet optimization and crowd movement in busy areas. However, existing research in congestion games assumes: (a) deterministic movement of agents between resources; and (b) perfect rationality (i.e. maximizing their own expected value) of all agents. Such assumptions are not reasonable in dynamic domains where decision support has to be provided to humans. For instance, in optimizing the performance of a taxi fleet serving a city, movement of taxis can be involuntary or nondeterministic (decided by the specific …