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Publications
Heterogeneous Crowd Simulation Using Parametric Reinforcement Learning
Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and animation of interacting digital humans. Model-… (voir plus)based approaches have successfully generated a plethora of simulators with a variety of foundations. However, prior approaches have been based on statically defined models predicated on simplifying assumptions, limited video-based datasets, or homogeneous policies. Recent works have applied reinforcement learning to learn policies for navigation. However, these approaches may learn static homogeneous rules, are typically limited in their generalization to trained scenarios, and limited in their usability in synthetic crowd domains. In this article, we present a multi-agent reinforcement learning-based approach that learns a parametric predictive collision avoidance and steering policy. We show that training over a parameter space produces a flexible model across crowd configurations. That is, our goal-conditioned approach learns a parametric policy that affords heterogeneous synthetic crowds. We propose a model-free approach without centralization of internal agent information, control signals, or agent communication. The model is extensively evaluated. The results show policy generalization across unseen scenarios, agent parameters, and out-of-distribution parameterizations. The learned model has comparable computational performance to traditional methods. Qualitatively the model produces both expected (laminar flow, shuffling, bottleneck) and unexpected (side-stepping) emergent qualitative behaviours, and quantitatively the approach is performant across measures of movement quality.
2021-12-29
IEEE Transactions on Visualization and Computer Graphics (published)
Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and animation of interacting digital humans. Model-… (voir plus)based approaches have successfully generated a plethora of simulators with a variety of foundations. However, prior approaches have been based on statically defined models predicated on simplifying assumptions, limited video-based datasets, or homogeneous policies. Recent works have applied reinforcement learning to learn policies for navigation. However, these approaches may learn static homogeneous rules, are typically limited in their generalization to trained scenarios, and limited in their usability in synthetic crowd domains. In this article, we present a multi-agent reinforcement learning-based approach that learns a parametric predictive collision avoidance and steering policy. We show that training over a parameter space produces a flexible model across crowd configurations. That is, our goal-conditioned approach learns a parametric policy that affords heterogeneous synthetic crowds. We propose a model-free approach without centralization of internal agent information, control signals, or agent communication. The model is extensively evaluated. The results show policy generalization across unseen scenarios, agent parameters, and out-of-distribution parameterizations. The learned model has comparable computational performance to traditional methods. Qualitatively the model produces both expected (laminar flow, shuffling, bottleneck) and unexpected (side-stepping) emergent qualitative behaviours, and quantitatively the approach is performant across measures of movement quality.
2021-12-29
IEEE Transactions on Visualization and Computer Graphics (publié)
Single Allocation Hub Location with Heterogeneous Economies of Scale
Borzou Rostami
Masoud Chitsaz
Okan Arslan
Gilbert Laporte
Andrea Lodi
The economies of scale in hub location is usually modeled by a constant parameter, which captures the benefits companies obtain through cons… (voir plus)olidation. In their article “Single allocation hub location with heterogeneous economies of scale,” Rostami et al. relax this assumption and consider hub-hub connection costs as piecewise linear functions of the flow amounts. This spoils the triangular inequality property of the distance matrix, making the classical flow-based model invalid and further complicates the problem. The authors tackle the challenge by building a mixed-integer quadratically constrained program and by developing a methodology based on constructing Lagrangian function, linear dual functions, and specialized polynomial-time algorithms to generate enhanced cuts. The developed method offers a new strategy in Benders-type decomposition through relaxing a set of complicating constraints in subproblems when such relaxation is tight. The results confirm the efficacy of the solution methods in solving large-scale problem instances.
Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There ha… (voir plus)ve been several proposals for efficient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing.
Fall 2021 Resurgence and COVID-19 Seroprevalence in Canada: Modelling waning and boosting COVID-19 immunity in Canada, A Canadian Immunization Research Network Study
The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-tw… (voir plus)entieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
Data deduplication has been widely used in backup systems to eliminate redundant data, which speeds up the backup process and reduces the st… (voir plus)orage overhead. Deduplication packs multiple chunks into a large, fixed-size container as a storage unit to maintain the locality and achieve efficient compression. We observe that the traditional containers have low filling ratios due to a large amount of metadata generated by small files. Unfilled containers require more space to store a backup, which decreases the storage efficiency and reduces restore performance. In order to address this problem, we propose a Metadata region Adaptive Container Structure, called MACS. MACS maintains a tag to record the length of metadata region in the container. The boundary between meta-data region and data region is dynamically decided to ensure the maximum space efficiency of the containers. Moreover, we propose a container metadata length-based indexing and cache replacement strategy to allow MACS to be practical in data backup systems. We demonstrate the advantages of MACS with three real world backup datasets. MACS achieves over 95% average container filling ratio, which is significantly higher than existing designs. MACS further achieves better restore performance than the traditional container structure. When combined with existing rewriting method, MACS achieves an efficient trade-off between deduplication ratio and restore performance.
2021-12-20
2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) (publié)
Dynamic games (also called stochastic games or Markov games) are an important class of games for modeling multi-agent interactions. In many … (voir plus)situations, the dynamics and reward functions of the game are learnt from past data and are therefore approximate. In this paper, we study the robustness of Markov perfect equilibrium to approximations in reward and transition functions. Using approximation results from Markov decision processes, we show that the Markov perfect equilibrium of an approximate (or perturbed) game is always an approximate Markov perfect equilibrium of the original game. We provide explicit bounds on the approximation error in terms of three quantities: (i) the error in approximating the reward functions, (ii) the error in approximating the transition function, and (iii) a property of the value function of the MPE of the approximate game. The second and third quantities depend on the choice of metric on probability spaces. We also present coarser upper bounds which do not depend on the value function but only depend on the properties of the reward and transition functions of the approximate game. We illustrate the results via a numerical example.
2021-12-20
2021 Seventh Indian Control Conference (ICC) (publié)
