This paper considers contextual bandits with a finite number of arms, where the contexts are independent and identically distributed $d$-dimensional random vectors, and the expected rewards are linear in both the arm parameters and contexts. The LinUCB algorithm, which is near minimax optimal for related linear bandits, is shown to have a cumulative regret that is suboptimal in both the dimension $d$ and time horizon $T$, due to its over-exploration. A truncated version of LinUCB is proposed and termed "Tr-LinUCB", which follows LinUCB up to a truncation time $S$ and performs pure exploitation afterwards. The Tr-LinUCB algorithm is shown to achieve $O(d\log(T))$ regret if $S = Cd\log(T)$ for a sufficiently large constant $C$, and a matching lower bound is established, which shows the rate optimality of Tr-LinUCB in both $d$ and $T$ under a low dimensional regime. Further, if $S = d\log^{\kappa}(T)$ for some $\kappa>1$, the loss compared to the optimal is a multiplicative $\log\log(T)$ factor, which does not depend on $d$. This insensitivity to overshooting in choosing the truncation time of Tr-LinUCB is of practical importance.

Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the curse of dimensionality, and scaling it to high-dimensional problems is still a challenge. In this paper, we propose a variable selection method MCTS-VS based on Monte Carlo tree search (MCTS), to iteratively select and optimize a subset of variables. That is, MCTS-VS constructs a low-dimensional subspace via MCTS and optimizes in the subspace with any BO algorithm. We give a theoretical analysis of the general variable selection method to reveal how it can work. Experiments on high-dimensional synthetic functions and real-world problems (i.e., NAS-bench problems and MuJoCo locomotion tasks) show that MCTS-VS equipped with a proper BO optimizer can achieve state-of-the-art performance.

These robots are subjected to the constraints of avoiding obstacles and maintaining connectivity of the structure. We develop two reconfiguration methods, one based on spatio-temporal planning, and one based on target swapping. Both methods achieve coordinated motion of robots by avoiding deadlocks and maintaining all constraints. Both methods also increase efficiency by reducing the amount of waiting times and lowering combined travel costs. The resulting progress is validated by simulations that also scale the number of robots.

This article was published as a part of the Data Science Blogathon. Until now, we have seen two different approaches to state space search. These search strategies compute the path to the goal state from the initial state. A* Search Strategy is one of the best strategies which provides near-optimum solutions. It uses a heuristic and actual cost function to reach the goal state with minimum cost.

Robots can complete all human-performed tasks, but due to their current lack of knowledge, some tasks still cannot be completed by them with a high degree of success. However, with the right knowledge, these tasks can be completed by robots with a high degree of success, reducing the amount of human effort required to complete daily tasks. In this paper, the FOON, which describes the robot action success rate, is discussed. The functional object-oriented network (FOON) is a knowledge representation for symbolic task planning that takes the shape of a graph. It is to demonstrate the adaptability of FOON in developing a novel and adaptive method of solving a problem utilizing knowledge obtained from various sources, a graph retrieval methodology is shown to produce manipulation motion sequences from the FOON to accomplish a desired aim. The outcomes are illustrated using motion sequences created by the FOON to complete the desired objectives in a simulated environment.

Humans tame the complexity of mathematical reasoning by developing hierarchies of abstractions. With proper abstractions, solutions to hard problems can be expressed concisely, thus making them more likely to be found. In this paper, we propose Learning Mathematical Abstractions (LEMMA): an algorithm that implements this idea for reinforcement learning agents in mathematical domains. LEMMA augments Expert Iteration with an abstraction step, where solutions found so far are revisited and rewritten in terms of new higher-level actions, which then become available to solve new problems. We evaluate LEMMA on two mathematical reasoning tasks--equation solving and fraction simplification--in a step-by-step fashion. In these two domains, LEMMA improves the ability of an existing agent, both solving more problems and generalizing more effectively to harder problems than those seen during training.

Visual Grounding (VG) in Visual Question Answering (VQA) systems describes how well a system manages to tie a question and its answer to relevant image regions. Systems with strong VG are considered intuitively interpretable and suggest an improved scene understanding. While VQA accuracy performances have seen impressive gains over the past few years, explicit improvements to VG performance and evaluation thereof have often taken a back seat on the road to overall accuracy improvements. A cause of this originates in the predominant choice of learning paradigm for VQA systems, which consists of training a discriminative classifier over a predetermined set of answer options. In this work, we break with the dominant VQA modeling paradigm of classification and investigate VQA from the standpoint of an information retrieval task. As such, the developed system directly ties VG into its core search procedure. Our system operates over a weighted, directed, acyclic graph, a.k.a. "lattice", which is derived from the scene graph of a given image in conjunction with region-referring expressions extracted from the question. We give a detailed analysis of our approach and discuss its distinctive properties and limitations. Our approach achieves the strongest VG performance among examined systems and exhibits exceptional generalization capabilities in a number of scenarios.

Search algorithms are applied where data retrieval with specified specifications is required. The motivation behind developing search algorithms in Functional Object-Oriented Networks is that most of the time, a certain recipe needs to be retrieved or ingredients for a certain recipe needs to be determined. According to the introduction, there is a time when execution of an entire recipe is not available for a robot thus prompting the need to retrieve a certain recipe or ingredients. With a quality FOON, robots can decipher a task goal, find the correct objects at the required states on which to operate and output a sequence of proper manipulation motions. This paper shows several proposed weighted FOON and task planning algorithms that allow a robot and a human to successfully complete complicated tasks together with higher success rates than a human doing them alone.

Lexically constrained text generation is one of the constrained text generation tasks, which aims to generate text that covers all the given constraint lexicons. While the existing approaches tackle this problem using a lexically constrained beam search algorithm or dedicated model using non-autoregressive decoding, there is a trade-off between the generated text quality and the hard constraint satisfaction. We introduce AutoTemplate, a simple yet effective lexically constrained text generation framework divided into template generation and lexicalization tasks. The template generation is to generate the text with the placeholders, and lexicalization replaces them into the constraint lexicons to perform lexically constrained text generation. We conducted the experiments on two tasks: keywords-to-sentence generations and entity-guided summarization. Experimental results show that the AutoTemplate outperforms the competitive baselines on both tasks while satisfying the hard lexical constraints.

Task competition by robots is still off from being completely dependable and usable. One way a robot may decipher information given to it and accomplish tasks is by utilizing FOON, which stands for functional object-oriented network. The network first needs to be created by having a human creates action nodes as well as input and output nodes in a .txt file. After the network is sizeable, utilization of this network allows for traversal of the network in a variety of ways such as choosing steps via iterative deepening searching by using the first seen valid option. Another mechanism is heuristics, such as choosing steps based on the highest success rate or lowest amount of input ingredients. Via any of these methods, a program can traverse the network given an output product, and derive the series of steps that need to be taken to produce the output.