Consider a two-person zero-sum search game between a hider and a searcher. The hider hides among $n$ discrete locations, and the searcher successively visits individual locations until finding the hider. Known to both players, a search at location $i$ takes $t_i$ time units and detects the hider -- if hidden there -- independently with probability $q_i$, for $i=1,\ldots,n$. The hider aims to maximize the expected time until detection, while the searcher aims to minimize it. We prove the existence of an optimal strategy for each player. In particular, the hider's optimal mixed strategy hides in each location with a nonzero probability, and the searcher's optimal mixed strategy can be constructed with up to $n$ simple search sequences. We develop an algorithm to compute an optimal strategy for each player, and compare the optimal hiding strategy with the simple hiding strategy which gives the searcher no location preference at the beginning of the search.

Path planning for multiple robots (MRPP) represents a task of finding non-colliding paths for robots through which they can navigate from their initial positions to specified goal positions. The problem is usually modeled using undirected graphs where robots move between vertices across edges. Contemporary optimal solving algorithms include dedicated search-based methods, that solve the problem directly, and compilation-based algorithms that reduce MRPP to a different formalism for which an efficient solver exists, such as constraint programming (CP), mixed integer programming (MIP), or Boolean satisfiability (SAT). In this paper, we enhance existing SAT-based algorithm for MRPP via spar-tification of the set of candidate paths for each robot from which target Boolean encoding is derived. Suggested sparsification of the set of paths led to smaller target Boolean formulae that can be constructed and solved faster while optimality guarantees of the approach have been kept.

Hyperparameter tuning is one of the fundamental steps in the machine learning routine. Also known as hyperparameter optimisation, the method entails searching for the best configuration of hyperparameters to enable optimal performance. Machine learning algorithms require user-defined inputs to achieve a balance between accuracy and generalisability. This process is known as hyperparameter tuning. There are various tools and approaches available to tune hyperparameters.

In the problem of active sequential hypotheses testing (ASHT), a learner seeks to identify the true hypothesis $h^*$ from among a set of hypotheses $H$. The learner is given a set of actions and knows the outcome distribution of any action under any true hypothesis. While repeatedly playing the entire set of actions suffices to identify $h^*$, a cost is incurred with each action. Thus, given a target error $\delta>0$, the goal is to find the minimal cost policy for sequentially selecting actions that identify $h^*$ with probability at least $1 - \delta$. This paper provides the first approximation algorithms for ASHT, under two types of adaptivity. First, a policy is partially adaptive if it fixes a sequence of actions in advance and adaptively decides when to terminate and what hypothesis to return. Under partial adaptivity, we provide an $O\big(s^{-1}(1+\log_{1/\delta}|H|)\log (s^{-1}|H| \log |H|)\big)$-approximation algorithm, where $s$ is a natural separation parameter between the hypotheses. Second, a policy is fully adaptive if action selection is allowed to depend on previous outcomes. Under full adaptivity, we provide an $O(s^{-1}\log (|H|/\delta)\log |H|)$-approximation algorithm. We numerically investigate the performance of our algorithms using both synthetic and real-world data, showing that our algorithms outperform a previously proposed heuristic policy.

Collaborative learning is gaining acceptance as one of the most successful educational approaches to learning. The basic idea is to organise learners in groups to work together and solve problems or complete tasks. There is ample evidence that when learners actively engage in discussions, listen to different viewpoints, and defend their positions, they better understand new concepts and learn faster. A particular case of collaborative learning is co-operative learning, where each student is responsible for at least one specific aspect or competence needed to solve the problem jointly. The student is improving her understanding through collaboration with others and is also responsible for the group's success concerning the aspect she is responsible for.

Scheduling computational tasks represented by directed acyclic graphs (DAGs) is challenging because of its complexity. Conventional scheduling algorithms rely heavily on simple heuristics such as shortest job first (SJF) and critical path (CP), and are often lacking in scheduling quality. In this paper, we present a novel learning-based approach to scheduling DAG tasks. The algorithm employs a reinforcement learning agent to iteratively add directed edges to the DAG, one at a time, to enforce ordering (i.e., priorities of execution and resource allocation) of "tricky" job nodes. By doing so, the original DAG scheduling problem is dramatically reduced to a much simpler proxy problem, on which heuristic scheduling algorithms such as SJF and CP can be efficiently improved. Our approach can be easily applied to any existing heuristic scheduling algorithms. On the benchmark dataset of TPC-H, we show that our learning based approach can significantly improve over popular heuristic algorithms and consistently achieves the best performance among several methods under a variety of settings.

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.