With the emergence of big data and modern technologies, artificial intelligence has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for artificial intelligence in fields such as robotics, predictive analytics, finance, and many more. So, if you're a developer who wants to upgrade your normal applications to smart and intelligent versions, then go for this Learning Path. Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

Referred to as an approach for either plan or goal recognition, the original method proposed by Ramirez and Geffner introduced a domain-based approach that did not need a library containing specific plan instances. This introduced a more generalizable means of representing tasks to be recognized, but was also very slow due to its need to run simulations via multiple executions of an off-the-shelf classical planner. Several variations have since been proposed for quicker recognition, but each one uses a drastically different approach that must sacrifice other qualities useful for processing the recognition results in more complex systems. We present work in progress that takes advantage of the shared state space between planner executions to perform multiple goal heuristic search. This single execution of a planner will potentially speed up the recognition process using the original method, which also maintains the sacrificed properties and improves some of the assumptions made by Ramirez and Geffner.

We study the problem of learning a good search policy. To do so, we propose the self-imitation learning setting, which builds upon imitation learning in two ways. First, self-imitation uses feedback provided by retrospective analysis of demonstrated search traces. Second, the policy can learn from its own decisions and mistakes without requiring repeated feedback from an external expert. Combined, these two properties allow our approach to iteratively scale up to larger problem sizes than the initial problem size for which expert demonstrations were provided.

According to the science magazine Nature, chemists are heralding a new artificial intelligence platform as a significant milestone. The platform has the potential to accelerate the process of drug discovery, and it should be able to make organic chemistry more efficient. The new platform is designed to help chemists to plan the syntheses of small organic molecules. Traditionally, chemists use the process of retrosynthesis, which is an established problem-solving technique whereby target molecules are recursively transformed into increasingly simpler precursors. The goal of retrosynthetic analysis is structural simplification.

Natural language processing tasks, such as caption generation and machine translation, involve generating sequences of words. Models developed for these problems often operate by generating probability distributions across the vocabulary of output words and it is up to decoding algorithms to sample the probability distributions to generate the most likely sequences of words. In this tutorial, you will discover the greedy search and beam search decoding algorithms that can be used on text generation problems. How to Implement Beam Search Decoder for Natural Language Processing Photo by See1,Do1,Teach1, some rights reserved. In natural language processing tasks such as caption generation, text summarization, and machine translation, the prediction required is a sequence of words.

One of the longest-running challenges in the logistics industry is finding the shortest routes. First articulated in the 1930s, the "traveling salesman problem" seeks to deduce the shortest route connecting a group of cities to ensure optimal use of time and resources. Karim Beguir, co-founder and CEO of London-based AI startup InstaDeep, told GPU Technology Conference attendees this week that GPU-powered deep learning and reinforcement learning may have the answer. Previous efforts to address the traveling salesman problem include optimization solvers, heuristics and Monte Carlo Tree Search algorithms. But, according to Beguir, these approaches all suffer from the same shortcoming: They don't learn.

The subgraph isomorphism problem involves deciding whether a copy of a pattern graph occurs inside a larger target graph. The non-induced version allows extra edges in the target, whilst the induced version does not. Although both variants are NP-complete, algorithms inspired by constraint programming can operate comfortably on many real-world problem instances with thousands of vertices. However, they cannot handle arbitrary instances of this size. We show how to generate "really hard" random instances for subgraph isomorphism problems, which are computationally challenging with a couple of hundred vertices in the target, and only twenty pattern vertices. For the non-induced version of the problem, these instances lie on a satisfiable / unsatisfiable phase transition, whose location we can predict; for the induced variant, much richer behaviour is observed, and constrainedness gives a better measure of difficulty than does proximity to a phase transition. These results have practical consequences: we explain why the widely researched "filter / verify" indexing technique used in graph databases is founded upon a misunderstanding of the empirical hardness of NP-complete problems, and cannot be beneficial when paired with any reasonable subgraph isomorphism algorithm.

We study estimation of (semi-)inner products between two nonparametric probability distributions, given IID samples from each distribution. These products include relatively well-studied classical $\mathcal{L}^2$ and Sobolev inner products, as well as those induced by translation-invariant reproducing kernels, for which we believe our results are the first. We first propose estimators for these quantities, and the induced (semi)norms and (pseudo)metrics. We then prove non-asymptotic upper bounds on their mean squared error, in terms of weights both of the inner product and of the two distributions, in the Fourier basis. Finally, we prove minimax lower bounds that imply rate-optimality of the proposed estimators over Fourier ellipsoids.

For quite a long time, a common opinion in academic world was that machine achieving human master performance level in the game of Go was far from realistic. It was considered a'holy grail' of AI – a milestone we were quite far away from reaching within upcoming decade. Deep Blue had its moment more than 20 years ago and since then no Go engine became close to human masters. The opinion about'numerical chaos' in Go established so well it became referenced in movies, too. Surprisingly, in march 2016 an algorithm invented by Google Deepmind called Alpha Go defeated korean world champion in Go 4-1 proving fictional and real-life sceptics wrong. Around a year after that, Alpha Go Zero – the next generation of Alpha Go Lee (the one beating Korean master) – was reported to destroy its predecessor 100-0, being very doubtfully reachable for humans.

This article presents the results of the first edition of the microRTS (μRTS) AI competition, which was hosted by the IEEE Computational Intelligence in Games (CIG) 2017 conference. The goal of the competition is to spur research on AI techniques for real-time strategy (RTS) games. In this first edition, the competition received three submissions, focusing on address- ing problems such as balancing long-term and short-term search, the use of machine learning to learn how to play against certain opponents, and finally, dealing with partial observability in RTS games.