In uninformed search, we do not look ahead of the goal. In other words, we do not ask the question "What is the cost of getting to the goal?". In order to guess the cost of getting to the goal from a state in a search, we need a heuristic function h(n), which is specific to the domain. In this way, the search will be more intelligent than the blind search. Instead of real cost functions of getting to the node, we consider heuristic function and estimates to get to the goal.

In this paper, we address the identical parallel machine scheduling problem with variable release dates and a common deadline for arrival. This problem occurs in several settings in which the release dates themselves are decision variables with the constraint that all jobs must arrive before or on a common fixed deadline. This deadline can be interpreted as a maximum release date for all jobs. To our knowledge, this problem has not been studied before in spite of many important applications. A first example is a manufacturing facility which uses a Just-In-Time discipline: jobs are released to the shop floor as late as possible to avoid cluttering the system but due to accounting restrictions, mostly related to the MRP (Materials Requirements Planning) logic, all work orders in a time bucket must be released before a fixed deadline. A second example is the receiving area of a warehouse which restricts the arrival of trucks within a time window. The warehouse may schedule its suppliers' trucks so to avoid congestion and provide them with an arrival time, but again, the warehouse's opening hours or external constraints such as circulation bans at certain hours, restrict the arrival of trucks. In these two examples, the deadline constraint cannot be violated, and a central controller must guarantee that all jobs meet such a constraint.

The internal structure of the model depends on the type of the There is a variety of approaches that can be used to learning algorithm, so complex data-driven models can consist identify the optimal design of the data-driven model. For of several semi-independent blocks - this approach is usually instance, AutoML solutions can be based on random search referred to as ensembling [2]. There are several techniques to [5], Bayesian optimisation [6], reinforcement learning (RL) build complex models: for example, blending allows creating [7], Monte Carlo tree search [8], sequential model-based single-level ensembles of machine learning (ML) models, and optimization [9], gradient-based approaches [10]. However, stacking allows creating multi-level ones. Other approaches are most of them are less flexible than evolutionary approaches to based on the representation of a model structure (or even the the model design (implemented e.g. in [11]). Their conceptual whole modeling pipeline) as a directed acyclic graph (DAG).

Preparing for an artificial intelligence job interview can feel overwhelming, whether you are a fresher or not. However, you need not worry much about this. In this article, Analytics Insight aims to familiarize its readers about the type of questions they can expect in the interview round. With artificial intelligence and machine learning touted as the most preferred and in-demand tech skill for 2021, it is important to access one's expertise in the same. Ever since the artificial intelligence started having a positive influence of the market, companies are on lookout to hire best professionals in the field.

Every blogger wants their blog to rank in the top position in Google search results since users commonly select results contained on the first page, especially those in one of the top 3 positions, as you can see in the graphic below. And, for years, the Google search algorithm made content king. This explains why companies invest more into content creation, with 24% of marketers planning to increase their budget for content marketing from 2020 levels. But, content creation is expensive; costing between $2000 and $10,000 a month for the average SME (small and mid-sized enterprise). If you want to get those costs down, consider using an AI-fueled content creator to make your job efficient at a lower cost. Moreover, if you write the content yourself, or you hire a writer to create content, it doesn't take long before you run out of topic ideas.

Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level decisions and task-level plan feasibility. TAMP approaches rely on domain-specific symbolic operators to guide the task-level search, making planning efficient. In this work, we formalize and study the problem of operator learning for TAMP. Central to this study is the view that operators define a lossy abstraction of the transition model of the underlying domain. We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system. Experimentally, we provide results in three domains, including long-horizon robotic planning tasks. We find our approach to substantially outperform several baselines, including three graph neural network-based model-free approaches based on recent work. Video: https://youtu.be/iVfpX9BpBRo

Automatic program repair (APR) is crucial to improve software reliability. Recently, neural machine translation (NMT) techniques have been used to fix software bugs automatically. While promising, these approaches have two major limitations. Their search space often does not contain the correct fix, and their search strategy ignores software knowledge such as strict code syntax. Due to these limitations, existing NMT-based techniques underperform the best template-based approaches. We propose CURE, a new NMT-based APR technique with three major novelties. First, CURE pre-trains a programming language (PL) model on a large software codebase to learn developer-like source code before the APR task. Second, CURE designs a new code-aware search strategy that finds more correct fixes by focusing on compilable patches and patches that are close in length to the buggy code. Finally, CURE uses a subword tokenization technique to generate a smaller search space that contains more correct fixes. Our evaluation on two widely-used benchmarks shows that CURE correctly fixes 57 Defects4J bugs and 26 QuixBugs bugs, outperforming all existing APR techniques on both benchmarks.

Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images. However, most Network Architecture Search (NAS) approaches in medical images focused on specific datasets and did not take into account the generalization ability of the learned architectures on unseen datasets as well as different domains. In this paper, we address this point by proposing to search for generalizable U-shape architectures on a composited dataset that mixes medical images from multiple segmentation tasks and domains creatively, which is named MixSearch. Specifically, we propose a novel approach to mix multiple small-scale datasets from multiple domains and segmentation tasks to produce a large-scale dataset. Then, a novel weaved encoder-decoder structure is designed to search for a generalized segmentation network in both cell-level and network-level. The network produced by the proposed MixSearch framework achieves state-of-the-art results compared with advanced encoder-decoder networks across various datasets.

Considering an environment containing polygonal obstacles, we address the problem of planning motions for a pair of planar robots connected to one another via a cable of limited length. Much like prior problems with a single robot connected via a cable to a fixed base, straight line-of-sight visibility plays an important role. The present paper shows how the reduced visibility graph provides a natural discretization and captures the essential topological considerations very effectively for the two robot case as well. Unlike the single robot case, however, the bounded cable length introduces considerations around coordination (or equivalently, when viewed from the point of view of a centralized planner, relative timing) that complicates the matter. Indeed, the paper has to introduce a rather more involved formalization than prior single-robot work in order to establish the core theoretical result -- a theorem permitting the problem to be cast as one of finding paths rather than trajectories. Once affirmed, the planning problem reduces to a straightforward graph search with an elegant representation of the connecting cable, demanding only a few extra ancillary checks that ensure sufficiency of cable to guarantee feasibility of the solution. We describe our implementation of A${}^\star$ search, and report experimental results. Lastly, we prescribe an optimal execution for the solutions provided by the algorithm.

If you a person who has ever tried to write a piece of code, you are sure to come across Stack Overflow (it's that famous). For those who live under a rock, Stack Overflow provides one of the largest QA platforms for programmers. Users post questions/doubts and their fellow peers try to provide solutions in the most helpful manner possible. The better an answer, the higher the votes it gets, which also increase a user's reputation. Given its popularity, it's safe to say that there is a buttload of data on there.