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.

Drew McDermott Research on planning for robots is in such a state of flux that there is disagreement about what planning is and whether it is necessary. We can take planning to be the optimization and debugging of a robot's program by reasoning about possible courses of execution. It is necessary to the extent that fragments of robot programs are combined at run time. There are several strands of research in the field; I survey six: (1) attempts to avoid planning; (2) the design of flexible plan notations; (3) theories of time-constrained planning; (4) planning by projecting and repairing faulty plans; (5) motion planning; and (6) the learning of optimal behaviors from reinforcements. More research is needed on formal semantics for robot plans.

To date, using Tesla's trip planning tool has meant sitting inside your electric car while you map a route that takes you past charging stations. That doesn't make much sense if you're gearing up for vacation, does it? There's now a better way: Tesla has launched a web version of its trip planner to use while you're still sitting at your desk. It's not as fleshed out as the in-car version, but it can tell you where you'll need to charge and how long you need to drive based on both the route and the particular Tesla you're driving. You could see fewer stops with a Model S P100D than you would with a Model X 75D, for instance. Convenience is clearly the main goal for the web planner, but it's also helpful if you're a prospective buyer.

There's More to Life Than Making Plans For many years, research in AI plan generation was governed by a number of strong, simplifying assumptions: The planning agent is omniscient, its actions are deterministic and instantaneous, its goals are fixed and categorical, and its environment is static. More recently, researchers have developed expanded planning algorithms that are not predicated on such assumptions, but changing the way in which plans are formed is only part of what is required when the classical assumptions are abandoned. The demands of dynamic, uncertain environments mean that in addition to being able to form plans--even probabilistic, uncertain plans--agents must be able to effectively manage their plans. In this article, which is based on a talk given at the 1998 AAAI Fall Symposium on Distributed, Continual Planning, we first identify reasoning tasks that are involved in plan management, including commitment management, environment monitoring, alternative assessment, plan elaboration, metalevel control, and coordination with other agents. We next survey approaches we have developed to many of these tasks and discuss a plan-management system we are building to ground our theoretical work, by providing us with a platform for integrating our techniques and exploring their value in a realistic problem.

The IJCAI-09 Workshop on Learning Structural Knowledge from Observations (STRUCK-09) took place as part of the International Joint Conference on Artificial Intelligence (IJCAI-09) on July 12 in Pasadena, California. The workshop program included paper presentations, discussion sessions about those papers, group discussions about two selected topics, and a joint discussion. As a result, many cognitive architectures use structural models to represent relations between knowledge of different complexity. Structural modeling has led to a number of representation and reasoning formalisms including frames, schemas, abstractions, hierarchical task networks (HTNs), and goal graphs among others. These formalisms have in common the use of certain kinds of constructs (for example, objects, goals, skills, and tasks) that represent knowledge of varying degrees of complexity and that are connected through structural relations.

The Find-Life-on-Mars event of the 1997 American Association for Artificial Intelligence Mobile Robot Competition and Exhibition featured robots trying to find and collect stationary and moving colored objects in an arena littered with real rocks. The 2-day event had 11 entries participating in both single-robot and multirobot categories, both with and without manipulators. During the event, many of the robots successfully demonstrated object recognition, obstacle avoidance, exploration, and the collection and depositing of objects. The general concept was to have the robots locate, collect, and deliver a variety of "life forms," including both stationary and moving objects. Technically, the event was designed to highlight mobile manipulation, object recognition, exploration, and obstacle avoidance in a relatively unstructured environment.

Although researchers have studied planning since the early days of AI, recent developments have revolutionized the field. Furthermore, work on propositional planning is closely related to the algorithms used in the autonomous controller for the National Aeronautics and Space Administration (NASA) Deep Space One spacecraft, launched in October 1998. As a result, our understanding of interleaved planning and execution has advanced as well as the speed with which we can solve classical planning problems. The goal of this survey is to explain these recent advances and suggest new directions for research. Because this article requires minimal AI background (for example, simple logic and basic search algorithms), it's suitable for a wide audience, but my treatment is not exhaustive because I don't have the space to discuss every active topic of planning research.