4 research outputs found

    Intelligent Traffic Management: From Practical Stochastic Path Planning to Reinforcement Learning Based City-Wide Traffic Optimization

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    This research focuses on intelligent traffic management including stochastic path planning and city scale traffic optimization. Stochastic path planning focuses on finding paths when edge weights are not fixed and change depending on the time of day/week. Then we focus on minimizing the running time of the overall procedure at query time utilizing precomputation and approximation. The city graph is partitioned into smaller groups of nodes and represented by its exemplar. In query time, source and destination pairs are connected to their respective exemplars and the path between those exemplars is found. After this, we move toward minimizing the city wide traffic congestion by making structural changes include changing the number of lanes, using ramp metering, varying speed limit, and modifying signal timing is possible. We propose a multi agent reinforcement learning (RL) framework for improving traffic flow in city networks. Our framework utilizes two level learning: a) each single agent learns the initial policy and b) multiple agents (changing the environment at the same time) update their policy based on the interaction with the dynamic environment and in agreement with other agents. The goal of RL agents is to interact with the environment to learn the optimal modification for each road segment through maximizing the cumulative reward over the set of possible actions in state space

    Decision Making Using Trust and Risk in Self-Adaptive Agent Organization

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    Self-organizing, multi-agent systems provide a suitable paradigm for agents to manage themselves. We demonstrate a robust, decentralized approach for structural adaptation in explicitly modelled problem-solving agent organizations. Based on self-organization principles, our method enables the agents to modify their structural relations to achieve a better completion rate of tasks in the environment. Reasoning on adaptation is based only on the agent\u27s history of interactions. Agents use the history of tasks assigned to their neighbors and completion rate as a measure of evaluation. This evaluation suggests the most suitable agents for reorganization (Meta-Reasoning). In the rst part of this research we propose Selective-Adaptation method. Our Selective-Adaptation has four different approaches of Meta-Reasoning, which are 1) Fixed Approach, 2) Need-Based Approach, 3) Performance- Based Approach, and 4) Satisfaction-Based Approach along with a Reorganization method, which needs less data but makes better decisions. Interaction between agents is one of the key factors in Multi-Agent societies. Using interaction, agents communicate with each other and cooperatively execute complex tasks which are beyond the capability of a single agent. Cooperatively executing tasks may endanger the success of an agent by selecting poor choices for peers. Therefore, agents need to have a better evaluation mechanism in selecting peers. Trust is one of the measures commonly used to evaluate the effectiveness of agents in cooperative societies. Since all of the interactions are subjected to uncertainty, the risk behavior of agents is considered as a contextual factor in decision making. In the second part of this research we propose the concept of adaptive risk and the use of recommendation-based trust in our adaptive society. We also introduce the agent\u27s strategy and propose an algorithm which helps agents to make decision in an adaptive society using adaptive risk and recommendation-based trust

    Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications

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    Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property prediction and brain connectome analysis. Importantly, recent works have shown that considering relationships between input data samples has a positive regularizing effect on the downstream task in healthcare applications. These relationships are naturally modeled by a (possibly unknown) graph structure between input samples. In this work, we propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications that exploits the graph representation of the input data samples and their latent relation. We assume an initially unknown latent-graph structure between graph-valued input data and propose to learn a parametric model for message passing within and across input graph samples, end-to-end along with the latent structure connecting the input graphs. Further, we introduce a Node Degree Distribution Loss (NDDL) that regularizes the predicted latent relationships structure. This regularization can significantly improve the downstream task. Moreover, the obtained latent graph can represent patient population models or networks of molecule clusters, providing a level of interpretability and knowledge discovery in the input domain, which is of particular value in healthcare
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