"Search is a problem-solving technique that systematically explores a space of problem states, i.e., successive and alternative stages in the problem-solving process. Examples of problem states might include the different board configurations in a game or intermediate steps in a reasoning process. This space of alternative solutions is then searched to find an answer. Newell and Simon (1976) have argued that this is the essential basis of human problem solving. Indeed, when a chess player examines the effects of different moves or a doctor considers a number of alternative diagnoses, they are searching among alternatives."
– from Section 1.2 of Chapter One of George F. Luger's textbook, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Edition (Addison-Wesley; 2005).
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.
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.
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.
The Rubik's Cube has been around for decades. I've toyed with the cube, probably in the very late '80s or early '90s, but never even imagined being able to solve one; from entirely shuffled, to perfectly ordered. But wouldn't it be satisfying if I could? Fortunately, the internet makes solving what was originally an architecture puzzle, doable for most of us. The world record for solving a cube has plummeted since 2000 from 20 seconds to under five, as pros and enthusiasts synthesized high-speed solutions and turn combinations (called algorithms) and shared them with the world.
However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation. Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations. One field of AI, called reinforcement learning, studies how computers can learn from their own experiences.
We study the problem of off-policy evaluation in the multi-armed bandit model with bounded rewards, and develop minimax rate-optimal procedures under three settings. First, when the behavior policy is known, we show that the Switch estimator, a method that alternates between the plug-in and importance sampling estimators, is minimax rate-optimal for all sample sizes. Second, when the behavior policy is unknown, we analyze performance in terms of the competitive ratio, thereby revealing a fundamental gap between the settings of known and unknown behavior policies. When the behavior policy is unknown, any estimator must have mean-squared error larger -- relative to the oracle estimator equipped with the knowledge of the behavior policy -- by a multiplicative factor proportional to the support size of the target policy. Moreover, we demonstrate that the plug-in approach achieves this worst-case competitive ratio up to a logarithmic factor. Third, we initiate the study of the partial knowledge setting in which it is assumed that the minimum probability taken by the behavior policy is known. We show that the plug-in estimator is optimal for relatively large values of the minimum probability, but is sub-optimal when the minimum probability is low. In order to remedy this gap, we propose a new estimator based on approximation by Chebyshev polynomials that provably achieves the optimal estimation error. Numerical experiments on both simulated and real data corroborate our theoretical findings.
We analyse the performance of several iterative algorithms for the quantisation of a probability measure $\mu$, based on the minimisation of a Maximum Mean Discrepancy (MMD). Our analysis includes kernel herding, greedy MMD minimisation and Sequential Bayesian Quadrature (SBQ). We show that the finite-sample-size approximation error, measured by the MMD, decreases as $1/n$ for SBQ and also for kernel herding and greedy MMD minimisation when using a suitable step-size sequence. The upper bound on the approximation error is slightly better for SBQ, but the other methods are significantly faster, with a computational cost that increases only linearly with the number of points selected. This is illustrated by two numerical examples, with the target measure $\mu$ being uniform (a space-filling design application) and with $\mu$ a Gaussian mixture.
The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research. This paper argues that games are an ideal domain for studying and experimenting with how humans interact with AI. Through a systematic survey of neural network games (n = 38), we identified the dominant interaction metaphors and AI interaction patterns in these games. In addition, we applied existing human-AI interaction guidelines to further shed light on player-AI interaction in the context of AI-infused systems. Our core finding is that AI as play can expand current notions of human-AI interaction, which are predominantly productivity-based. In particular, our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction, incorporate discovery-based learning to play around with the AI and observe the consequences, and offer users an invitation to play to explore new forms of human-AI interaction.
Width-based search methods have demonstrated state-of-the-art performance in a wide range of testbeds, from classical planning problems to image-based simulators such as Atari games. These methods scale independently of the size of the state-space, but exponentially in the problem width. In practice, running the algorithm with a width larger than 1 is computationally intractable, prohibiting IW from solving higher width problems. In this paper, we present a hierarchical algorithm that plans at two levels of abstraction. A high-level planner uses abstract features that are incrementally discovered from low-level pruning decisions. We illustrate this algorithm in classical planning PDDL domains as well as in pixel-based simulator domains. In classical planning, we show how IW(1) at two levels of abstraction can solve problems of width 2. For pixel-based domains, we show how in combination with a learned policy and a learned value function, the proposed hierarchical IW can outperform current flat IW-based planners in Atari games with sparse rewards.