Mattmüller et al. (2010) is a planner based on an adapted version of Hansen and Zilberstein (2001), a heuristic search algorithm that has theoretical guarantees to extract strong cyclic solutions for Markov decision problems. Kuter et al. (2008) makes use of Classical Planning algorithms to solve planning tasks. Fu et al. (2011) is similar to, but the main difference is that avoids exploring already explored/solved states, being more efficient than . Muise et al. (2012) is one the most efficient planners in the literature, and it is built upon some improvements over the state relevance techniques, such as avoiding dead-ends states. The main idea of these planners is selecting a reachable state s by the current policy that still is undefined in the current policy.

This work addresses the problem of exploration in an unknown environment. For linear dynamical systems, we use an experimental design framework and introduce an online greedy policy where the control maximizes the information of the next step. In a setting with a limited number of experimental trials, our algorithm has low complexity and shows experimentally competitive performances compared to more elaborate gradient-based methods.

Traditional drug discovery techniques are all about brute force--and a little bit of luck. Basically, large-scale, high-throughput screening is used to cover a search space. The process is a little like conducting antisubmarine warfare without the benefit of sonar. Unsurprisingly, very few of the depth charges (drug candidates) hit their targets and achieve the desired results (successful clinical trials). The seas are simply too vast.

But what is the Google Search Algorithm? And, most importantly, how can you rank higher on Google and get more traffic? The Google Search Algorithm refers to the process Google uses to rank content. It takes into account hundreds of factors, including keyword mentions, usability, and backlinks. Google has multiple search algorithms all working together to return the best results. In this article, we'll focus mainly on Google's ranking algorithm(s), as we believe that's what most people are referring to when they talk about Google's search algorithm.

The race between technology and human beings has been a recurring theme in science fiction for more than a century. With human civilization witnessing phenomenal and unforeseen growth in high tech in the last few decades, the experiments of technology competing with the human agency have become much sharper and more frequent. There is no prize for guessing that much of it is occurring due to the exponential growth of AI. Recently, an artificial intelligence machine won the Paris Bridge Competition over human players and now the future in the gaming industry is dicey for humans. A very recent addition to such a stream of experimental exercises was found in Paris.

Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices. While most variational quantum algorithms involve only continuous optimization variables, the representational power of the variational ansatz can sometimes be significantly enhanced by adding certain discrete optimization variables, as is exemplified by the generalized quantum approximate optimization algorithm (QAOA). However, the hybrid discrete-continuous optimization problem in the generalized QAOA poses a challenge to the optimization. We propose a new algorithm called MCTS-QAOA, which combines a Monte Carlo tree search method with an improved natural policy gradient solver to optimize the discrete and continuous variables in the quantum circuit, respectively. We find that MCTS-QAOA has excellent noise-resilience properties and outperforms prior algorithms in challenging instances of the generalized QAOA.

Optimization strategies driven by machine learning, such as Bayesian optimization, are being explored across experimental sciences as an efficient alternative to traditional design of experiment. When combined with automated laboratory hardware and high-performance computing, these strategies enable next-generation platforms for autonomous experimentation. However, the practical application of these approaches is hampered by a lack of flexible software and algorithms tailored to the unique requirements of chemical research. One such aspect is the pervasive presence of constraints in the experimental conditions when optimizing chemical processes or protocols, and in the chemical space that is accessible when designing functional molecules or materials. Although many of these constraints are known a priori, they can be interdependent, non-linear, and result in non-compact optimization domains. In this work, we extend our experiment planning algorithms Phoenics and Gryffin such that they can handle arbitrary known constraints via an intuitive and flexible interface. We benchmark these extended algorithms on continuous and discrete test functions with a diverse set of constraints, demonstrating their flexibility and robustness. In addition, we illustrate their practical utility in two simulated chemical research scenarios: the optimization of the synthesis of o-xylenyl Buckminsterfullerene adducts under constrained flow conditions, and the design of redox active molecules for flow batteries under synthetic accessibility constraints. The tools developed constitute a simple, yet versatile strategy to enable model-based optimization with known experimental constraints, contributing to its applicability as a core component of autonomous platforms for scientific discovery.

In this paper, we give nearly minimax optimal algorithms for multi-task linear bandits with shared representation. Multi-task representation learning learns a joint low-dimensional feature extractor from different but related tasks, so the composition of this feature extractor and a simple function (e.g., linear function) can give more accurate predictions than the standard single-task learning paradigm [Baxter, 2000; Caruana, 1997]. The fundamental reason for this improvement is that the relatedness among tasks make us learn the joint feature extractor more efficiently than treating each task independently. Empirically, representation learning has led to successes in applications such as computer vision [Li et al., 2014], natural language processing [Ando and Zhang, 2005; Liu et al., 2019], and drug discovery [Ramsundar et al., 2015].

Quantum computing continues to advance at a rapid pace, but one challenge that holds the field back is mitigating the noise that plagues quantum machines. This leads to much higher error rates compared to classical computers. This noise is often caused by imperfect control signals, interference from the environment, and unwanted interactions between qubits, which are the building blocks of a quantum computer. Performing computations on a quantum computer involves a "quantum circuit," which is a series of operations called quantum gates. These quantum gates, which are mapped to the individual qubits, change the quantum states of certain qubits, which then perform the calculations to solve a problem.

Motivated by a real-world vehicle routing application, we consider the maximum-weight independent set problem: Given a node-weighted graph, find a set of independent (mutually nonadjacent) nodes whose node-weight sum is maximum. Some of the graphs airsing in this application are large, having hundreds of thousands of nodes and hundreds of millions of edges. To solve instances of this size, we develop a new local search algorithm, which is a metaheuristic in the greedy randomized adaptive search (GRASP) framework. This algorithm, which we call METAMIS, uses a wider range of simple local search operations than previously described in the literature. We introduce data structures that make these operations efficient. A new variant of path-relinking is introduced to escape local optima and so is a new alternating augmenting-path local search move that improves algorithm performance. We compare an implementation of our algorithm with a state-of-the-art openly available code on public benchmark sets, including some large instances with hundreds of millions of vertices. Our algorithm is, in general, competitive and outperforms this openly available code on large vehicle routing instances. We hope that our results will lead to even better MWIS algorithms.