This paper defines a learning algorithm for plan grammars used for plan recognition. The algorithm learns Combinatory Categorial Grammars (CCGs) that capture the structure of plans from a set of successful plan execution traces paired with the goal of the actions. This work is motivated by past work on CCG learning algorithms for natural language processing, and is evaluated on five well know planning domains.

This paper proposes and evaluates Memory-Augmented Monte Carlo Tree Search (M-MCTS), which provides a new approach to exploit generalization in online real-time search. The key idea of M-MCTS is to incorporate MCTS with a memory structure, where each entry contains information of a particular state. This memory is used to generate an approximate value estimation by combining the estimations of similar states. We show that the memory based value approximation is better than the vanilla Monte Carlo estimation with high probability under mild conditions. We evaluate M-MCTS in the game of Go. Experimental results show that M-MCTS outperforms the original MCTS with the same number of simulations.

Scenario planning is a commonly used method by companies to develop their long-term plans. Scenario planning for risk management puts an added emphasis on identifying and managing emerging risk. While a variety of methods have been proposed for this purpose, we show that applying AI planning techniques to devise possible scenarios provides a unique advantage for scenario planning. Our system, the Scenario Planning Advisor (SPA), takes as input the relevant information from news and social media, representing key risk drivers, as well as the domain knowledge and generates scenarios that explain the key risk drivers and describe the alternative futures. To this end, we provide a characterization of the problem, knowledge engineering methodology, and transformation to planning. Furthermore, we describe the computation of the scenarios, lessons learned, and the feedback received from the pilot deployment of the SPA system in IBM.

We consider the problem of constructing abstract representations for planning in high-dimensional, continuous environments. We assume an agent equipped with a collection of high-level actions, and construct representations provably capable of evaluating plans composed of sequences of those actions. We first consider the deterministic planning case, and show that the relevant computation involves set operations performed over sets of states. We define the specific collection of sets that is necessary and sufficient for planning, and use them to construct a grounded abstract symbolic representation that is provably suitable for deterministic planning. The resulting representation can be expressed in PDDL, a canonical high-level planning domain language; we construct such a representation for the Playroom domain and solve it in milliseconds using an off-the-shelf planner. We then consider probabilistic planning, which we show requires generalizing from sets of states to distributions over states. We identify the specific distributions required for planning, and use them to construct a grounded abstract symbolic representation that correctly estimates the expected reward and probability of success of any plan. In addition, we show that learning the relevant probability distributions corresponds to specific instances of probabilistic density estimation and probabilistic classification. We construct an agent that autonomously learns the correct abstract representation of a computer game domain, and rapidly solves it. Finally, we apply these techniques to create a physical robot system that autonomously learns its own symbolic representation of a mobile manipulation task directly from sensorimotor data---point clouds, map locations, and joint angles---and then plans using that representation. Together, these results establish a principled link between high-level actions and abstract representations, a concrete theoretical foundation for constructing abstract representations with provable properties, and a practical mechanism for autonomously learning abstract high-level representations.

A recent press release states, "ClickSoftware, the leading provider of field service management software, today announced significant new capabilities for Field Service Edge, its cloud-based, mobile workforce management platform designed to meet the needs of the most demanding field service organizations. This latest offering introduces new strategic capabilities that will greatly improve field service efficiency and effectiveness. Major features are: (1) Predictive field service powered by ClickSoftware's Machine Learning Cloud, which identifies data patterns to make predictions and automatically improve valuable KPIs, (2) New demand forecasting capabilities to support more accurate resource planning and schedule optimization and provide richer insights to support proper staffing.

Addressing human needs must be top priorities to design, develop and implement successful AI technology. With a rising amount of media attention and a sevenfold increase of investment, artificial intelligence (AI) is on the agenda of businesses in all industries, who are identifying the massive potential with this kind of technology. Design sits at the forefront of the drive to develop innovative everyday AI solutions that people find intuitive and easy to use. To pave the way for successful AI implementation, companies must consider creating experiences with AI that are less artificial and more intelligent, and most importantly, those that make AI more human-shaped. This is where design comes in. Designers possess the skills to create a world where everything is designed around real human needs.

The concept of artificial intelligence (AI), described as the development of computer systems capable of performing tasks which, in general, require human intelligence, arose more than 60 years ago.However, this technology did not reach the field of financial services until the early 1980s.It had limited implementation and use at that time due to the state of the technology and power of computer systems. But in recent years there has been significant growth in injecting AI into the financial planning process. In the early 1980s, for example, the Citibank Investment Bank attempted to build expert systems, using artificial intelligence that imitated the decision-makingpower of a human expert. And Citibank was not the only one, many other Wall Street companies launched similar projects at that time. And in 1987, the Security Pacific National Bank launched a Fraud Prevention Task Force to automatically counter, through the use of artificial intelligence, unauthorized use of debit cards at ATMs and stores.

A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion.

The subject of game AI generally begins with so-called perfect information games. These are turn-based games where the players have no information hidden from each other and there is no element of chance in the game mechanics (such as by rolling dice or drawing cards from a shuffled deck). Tic Tac Toe, Connect 4, Checkers, Reversi, Chess, and Go are all games of this type. Because everything in this type of game is fully determined, a tree can, in theory, be constructed that contains all possible outcomes, and a value assigned corresponding to a win or a loss for one of the players. Finding the best possible play, then, is a matter of doing a search on the tree, with the method of choice at each level alternating between picking the maximum value and picking the minimum value, matching the different players' conflicting goals, as the search proceeds down the tree.

September through December are the busiest cargo shipping months of the year thanks to the winter holiday season, and in 2017, that was even more true than usual. The demand for shipping space on container ships, and the pace of arrivals at commercial ports, can hit companies with time-consuming and expensive issues: shipment delays, required changes in shipping method from marine to air, scheduling problems for the unloading and reloading of containers, and freight theft. In a retail environment where Amazon and other large retailers offer quick shipping, for free, manufacturers and retailers now risk losing money -- and customers -- if deliveries are delayed. Increasingly, the commercial shipping firms that retailers and manufacturers rely on to get products from A to B are turning to new technologies like artificial intelligence and automation to analyze the huge amounts of data generating in shipping, with an eye toward streamlining the processes, anticipating potential delays, and saving money. For an industry that has used some of the same systems for years, artificial intelligence and automation offer an opportunity for revolution.