NEW YORK, NY - JUNE 16: Director of Facebook AI Research Yann LeCun attends the 2016 Wired Business Conference on June 16, 2016 in New York City. Renowned artificial intelligence (AI) experts from almost all of the tech giants are planning to boycott a new journal from Nature Publishing Group, which is widely regarded as one of the most influential science publishers in the world. Nature's new Machine Intelligence Journal is due to be published for the first time in January 2019. Nature said it will cover the "best research from across the field of artificial intelligence" but it will also be a closed access journal, and this has angered many in the AI community who want to see AI research openly available to everyone. Over 2,000 people -- including more than 75 from Google, 25 from Microsoft, 23 from DeepMind, 16 from Facebook, and 11 from Amazon -- have pledged to "not submit to, review, or edit for this new journal".

We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. We show that a deep neural network can be used to learn and represent a \emph{generalized reactive policy} (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances. In contrast to prior efforts in this direction, our approach significantly reduces the dependence of learning on handcrafted domain knowledge or feature selection. Instead, the GRP is trained from scratch using a set of successful execution traces. We show that our approach can also be used to automatically learn a heuristic function that can be used in directed search algorithms. We evaluate our approach using an extensive suite of experiments on two challenging planning problem domains and show that our approach facilitates learning complex decision making policies and powerful heuristic functions with minimal human input. Videos of our results are available at goo.gl/Hpy4e3.

Artificial intelligence is either the greatest thing to ever happen to human work or the dread of our existence. This independently written report explores how AI will reshape the workplace and how analytically minded individuals can stand out. The report also provides tips on how to address security risks when using one type of AI -- internet-connected smart speakers -- in the office. Please fill out this form to instantly receive the report PDF at the email you enter below. By filling out this form you agree to be contacted by our trusted sponsors.

This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigation applications, individualized wayfinding for people with disabilities (e.g., vision impairments, physical disabilities, etc.), path planning for evacuations, robotic navigations, and path planning for autonomous vehicles. We propose an architecture based on GANs to recommend accurate and reliable paths for navigation applications. The proposed system can use crowd-sourced data to learn the trajectories and infer new ones. The system provides users with generated paths that help them navigate from their local environment to reach a desired location. As a use case, we experimented with the proposed method in support of a wayfinding application in an indoor environment. Our experiments assert that the generated paths are correct and reliable. The accuracy of the classification task for the generated paths is up to 99% and the quality of the generated paths has a mean opinion score of 89%.

General purpose planners enable AI systems to solve many different types of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this paper, we empirically compare the performance of state-of-the-art planners that use either the Planning Domain Description Language (PDDL), or Answer Set Programming (ASP) as the underlying action language. PDDL is designed for automated planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used for solving planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general purpose planning systems for a particular domain.

In the previous blogs, I have been talking about what artificial intelligence (AI) is and some of the hype and myths behind it. One of the observations was that for me, for IFS, AI is very much a means to a goal. It's about applying to a very specific use case to deliver better results. One of these use cases is about workforce optimization. For more than 12 years, IFS has been using machine learning algorithms and other smart techniques inside IFS Planning & Scheduling Optimization (PSO) to provide optimized schedules for our customers.

One of the most important undertakings an enterprise has to deal with is finding and hiring an amazing candidate. But the fierce competition for talent, lackluster recruiting and sourcing processes and uncoordinated evaluation criteria make it an arduous task. Astoundingly, 74% of employers claim they've hired the wrong person for a position. These hires come at a cost, with organizations losing an average of nearly $15,000 on every bad hire. It's enticing to tap into artificial intelligence to help with hiring, managing and optimizing contingent workers.

Planning with preferences has been employed extensively to quickly generate high-quality plans. However, it may be difficult for the human expert to supply this information without knowledge of the reasoning employed by the planner and the distribution of planning problems. We consider the problem of actively eliciting preferences from a human expert during the planning process. Specifically, we study this problem in the context of the Hierarchical Task Network (HTN) planning framework as it allows easy interaction with the human. Our experimental results on several diverse planning domains show that the preferences gathered using the proposed approach improve the quality and speed of the planner, while reducing the burden on the human expert.

We consider planning problems for graphs, Markov decision processes (MDPs), and games on graphs. While graphs represent the most basic planning model, MDPs represent interaction with nature and games on graphs represent interaction with an adversarial environment. We consider two planning problems where there are k different target sets, and the problems are as follows: (a) the coverage problem asks whether there is a plan for each individual target set; and (b) the sequential target reachability problem asks whether the targets can be reached in sequence. For the coverage problem, we present a linear-time algorithm for graphs, and quadratic conditional lower bound for MDPs and games on graphs. For the sequential target problem, we present a linear-time algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic conditional lower bound for games on graphs. Our results with conditional lower bounds establish (i) modelseparation results showing that for the coverage problem MDPs and games on graphs are harder than graphs, and for the sequential reachability problem games on graphs are harder than MDPs and graphs; and (ii) objective-separation results showing that for MDPs the coverage problem is harder than the sequential target problem.

In order to make the task, description of planning domains and problems, more comprehensive for non-experts in planning, the visual representation has been used in planning domain modeling in recent years. However, current knowledge engineering tools with visual modeling, like itSIMPLE (Vaquero et al. 2012) and VIZ (Vodr\'a\v{z}ka and Chrpa 2010), are less efficient than the traditional method of hand-coding by a PDDL expert using a text editor, and rarely involved in finetuning planning domains depending on the plan validation. Aim at this, we present an integrated development environment KAVI for planning domain modeling inspired by itSIMPLE and VIZ. KAVI using an abstract domain knowledge base to improve the efficiency of planning domain visual modeling. By integrating planners and a plan validator, KAVI proposes a method to fine-tune planning domains based on the plan validation.