Network-aware multi-agent reinforcement learning for the vehicle navigation problem

Arasteh, Fazel; SheikhGarGar, Soroush; Papagelis, Manos

doi:10.1145/3557915.3561005

Cited by 2 publications

(2 citation statements)

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“…For this scenario, we used a traffic dataset generated through the Simulation of Urban MObility (SUMO) framework [19]. Thus, we used the net file with the Downtown Toronto abstraction made in [20] to identify congestion points on city roads, as revealed in Figure 2. The simulation map has 52 intersections and roads with 1 or 2 lanes.…”

Section: Scenariomentioning

confidence: 99%

Uncertainty measurement for complex event prediction in safety-critical systems

Peixoto,

Azim

2023

Preprint

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Complex events originate from other primitive events combined according to defined patterns and rules. Instead of using specialists' manual work to compose the model rules, we use machine learning (ML) to self-define these patterns and regulations based on incoming input data to produce the desired complex event. Complex events processing (CEP) uncertainty is critical for embedded and safety-critical systems. This paper exemplifies how we can measure uncertainty for the perception and prediction of events, encompassing embedded systems that can also be critical to safety. Then, we propose an approach (ML\_CP) incorporating ML and sensitivity analysis that verifies how the output varies according to each input parameter. Furthermore, our model also measures the uncertainty associated with the predicted complex event. Therefore, we use conformal prediction to build prediction intervals, as the model itself has uncertainties, and the data has noise. Also, we tested our approach with classification (binary and multi-level) and regression problems test cases. Finally, we present and discuss our results, which are very promising within our field of research and work.

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Section: Scenariomentioning

confidence: 99%

Uncertainty measurement for complex event prediction in safety-critical systems

Peixoto,

Azim

2023

Preprint

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“…Trajectory data mining involves extracting insights and patterns from large-scale mobility data. It aims to uncover hidden relationships and insights into mobility patterns and behaviors, with the goal of supporting a wide range of applications, including transportation planning [9], location-based services, urban planning [10], and public health monitoring [9,11,12]. Of particular interest are technical problems related to trajectory similarity [13], trajectory clustering [14], anomaly detection in moving objects [15], and graph-related problems, such as finding important nodes in mobility networks [16], and mining interactions of moving objects or people [17][18][19].…”

Section: Trajectory Data Miningmentioning

confidence: 99%

Trajectory-User Linking using Higher-order Mobility Flow Representations

Alsaeed,

Agrawal,

Papagelis

2023

2023 24th IEEE International Conference on Mobile Data Management (MDM)

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Trajectory-user linking (TUL) is a problem in trajectory classification that links anonymous trajectories to the users who generated them. TUL has various uses such as identity verification, personalized recommendation, epidemiological monitoring, and threat assessments. A major challenge in TUL modeling is sparse data.Previous TUL research heavily relies on recurrent neural networks models such as RNNs and LSTMs, with trajectory segmentation to combat sparsity, but segmentation does not sufficiently address the issue and existing models often ignore data skewness, resulting in poor precision and performance. To address these problems, we present TULHOR, a TUL model inspired by BERT, a popular language representation model. One of TULHOR's innovations is the use of higher-order mobility flow data representations enabled by geographic area tessellation. This allows the model to alleviate the sparsity problem and also to generalize better. TULHOR consists of a spatial embedding layer, a spatial-temporal embedding layer and an encoder layer, which encodes properties and learns a rich trajectory representation. It is trained in two steps, first using a masked language modeling task to learn general embeddings, then fine-tuned using a balanced cross-entropy loss to make predictions while handling imbalanced data. Experiments on real-life mobility data show TULHOR's effectiveness as compared to current state-of-the-art models.ii To Mom, Dad and my grandparents iii Praise be to Allah, the Most Gracious, the Most Merciful. Prayer and peace be upon the Prophet Muhammad. First of all, I am grateful to my supervisor, Manos Papagelis, for his unwavering guidance, remarkable patience, tireless support, and invaluable advice throughout my studies. His contributions played a significant role in the success of this work, and I would not have been able to complete it without his help. Thanks to Ameeta Agrawal for her help and guidance for the past two years. I would like to thank committee members Aijun An and Mehdi Nourinejad for taking the time to read my thesis and for providing feedback. I would like to express my deepest gratitude to my parents, grandparents, brothers, and sisters for their love and support throughout this journey. I would like to extend my sincere appreciation to my friends, whose company may have delayed my graduation and reduced my productivity in terms of published works. However, their friendship was invaluable and made the journey worthwhile. In addition, I would like to express my gratitude to my cats who provided me with emotional support during the course of my studies. Although they could be a distraction at times, their positive impact cannot be overstated.

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Network-aware multi-agent reinforcement learning for the vehicle navigation problem

Cited by 2 publications

References 4 publications

Uncertainty measurement for complex event prediction in safety-critical systems

Uncertainty measurement for complex event prediction in safety-critical systems

Trajectory-User Linking using Higher-order Mobility Flow Representations

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