An Online Decision-Theoretic Pipeline for Responder Dispatch
| Autor: | Chinmaya Samal, Abhishek Dubey, Geoffrey Pettet, Yevgeniy Vorobeychik, Ayan Mukhopadhyay |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Service (systems architecture) Computer Science - Machine Learning Operations research Computer science Heuristic Computer Science - Artificial Intelligence media_common.quotation_subject Systems and Control (eess.SY) 02 engineering and technology Pipeline (software) Field (computer science) Machine Learning (cs.LG) Artificial Intelligence (cs.AI) 020901 industrial engineering & automation Scalability FOS: Electrical engineering electronic engineering information engineering Computer Science - Systems and Control Quality (business) Computer Science - Multiagent Systems Multiagent Systems (cs.MA) media_common |
| Popis: | The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time. Appeared in ICCPS 2019 |
| Databáze: | OpenAIRE |
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