The overall network architecture is shown in Figure 1. This work was done when the author was with Rutgers University. The overall network architecture is shown in Figure 1. We also apply the ReLU activation after its first and second layers. Empirical evaluations show that NHE exhibits admissibility and consistency.

The overall network architecture is shown in Figure 1. This work was done when the author was with Rutgers University. The overall network architecture is shown in Figure 1. We also apply the ReLU activation after its first and second layers. Empirical evaluations show that NHE exhibits admissibility and consistency.

In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Model robustness and generalization are assessed using cross-validation techniques. To evaluate the performance of models, we use Mean Squared Error (MSE) and R2. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The study identifies the most influential project attributes in determining the magnitude of cost and schedule deviations caused by scope modifications. It is identified that productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are powerful predictors.

Multi-agent systems can be extremely efficient when working concurrently and collaboratively, e.g., for transportation, maintenance, search and rescue. Coordination of such teams often involves two aspects: (i) selecting appropriate sub-teams for different tasks; (ii) designing collaborative control strategies to execute these tasks. The former aspect can be combinatorial w.r.t. the team size, while the latter requires optimization over joint state-spaces under geometric and dynamic constraints. Existing work often tackles one aspect by assuming the other is given, while ignoring their close dependency. This work formulates such problems as combinatorial-hybrid optimizations (CHO), where both the discrete modes of collaboration and the continuous control parameters are optimized simultaneously and iteratively. The proposed framework consists of two interleaved layers: the dynamic formation of task coalitions and the hybrid optimization of collaborative behaviors. Overall feasibility and costs of different coalitions performing various tasks are approximated at different granularities to improve the computational efficiency. At last, a Nash-stable strategy for both task assignment and execution is derived with provable guarantee on the feasibility and quality. Two non-trivial applications of collaborative transportation and dynamic capture are studied against several baselines.

This article will cover the widely used technique to avoid overfitting. Deep neural networks tend to overfit because of their complexity, large number of hidden layers, where the training error is very small but the testing error is may go up. Regularization helps the model to generalize better so that it performs better with unseen data. Regularization introduces uncertainty or randomness to the learning algorithm, it also simplifies the neural network. Some of the regularization techniques penalize the weight metrics for being too large some techniques reduce the number of hidden units in the Neural Network. There are different types of regularization techniques that affect the model very differently.

The time, capital and personnel required to get basic AI technologies running in-house underscores why such implementation is limited to legal teams. Because of the heavy lifting and dedicated resources an AI implementation can take up, most early adopters are likely to be large corporations for whom AI can provide the most benefit for its cost. In addition to Cisco, McCarron noted that there are several other "larger behemoth" companies road mapping and implementing AI projects, noting Google's work to bring AI contract solutions from Seal Software into their legal operations as an example. But medium-sized companies like PayPal and eBay "are definitely not doing it" yet, she added, an indication that the resources needed for AI may still be too cost-prohibitive for some. So while the technology is still young and the market still evolving, for now, excitement over AI's ability to greatly modernize the legal industry is likely to be tempered by the reality of getting it up and running.

Partially Observable Markov Decision Processes (POMDPs) are often used to model planning problems under uncertainty. The goal in Risk-Sensitive POMDPs (RS-POMDPs) is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, unlike existing POMDP literature, we distinguish between the two cases of whether costs can or cannot be observed and show the empirical impact of cost observations. We also introduce a new search-based algorithm to solve RS-POMDPs and show that it is faster and more scalable than existing approaches in two synthetic domains and a taxi domain generated with real-world data.