Kayak has rolled out a new tool for those who like to make travel plans when they're supposed to be working. The popular search engine has designed a platform that looks like a spreadsheet instead of a travel search site. The tool, called @work, or At Work, features search criteria discretely placed in the rows and columns of a convincing spreadsheet titled "Travel Problem Solved Report." Whether or not this will fool the boss -- or get you fired -- remains to be seen. The company says in press materials that it's just trying to help workers out.

This is my 2nd publication in field of Artificial Intelligence, prepared as a part of my project in AI Nanodegree classes. This time the goal was to write research paper about important historical developments in the field of AI planning and search. I hope you will like it . Planning or more precisely: automated planning and scheduling is one of the major fields of AI (among the others like: Machine Learning, Natural Language Processing, Computer Vision and more). To accomplish given tasks, these systems need to have input data containing descriptions of initial states of the world, desired goals and actions.

The available tools and support for building planning and scheduling systems and applications have been steadily improving for decades. At the same time, the scope, scale, and complexity of the problems to be addressed has been increasing. In this column, I discuss several different scheduling applications developed over the past 25 years, and then describe the tools and techniques used in addressing these problems, showing how improved tools simplified (and in some cases enabled) the solution of problems of increasing difficulty.

We review the 2016 International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS), the fifth in a series of competitions started in 2005. ICKEPS series focuses on promoting the importance of knowledge engineering methods and tools for automated Planning and Scheduling systems.

Neither of these terms are fundamental categories. The initial AIMS scheduling problem encompassed 29,000 discrete activities, subject to 97,000 complex metric constraints specified by AIMS applications developers. Generating feasible schedules was an essential requirement for operating the 777, potentially threatening a Boeing investment of almost 10 billion dollars. The scale and complexity of this problem were unprecedented, and there were very few applicable tools or standards. Input requirements were provided as text, with a semantics negotiated and maintained through frequent discussion.

We review the 2016 International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS), the fifth in a series of competitions started in 2005. ICKEPS series focuses on promoting the importance of knowledge engineering methods and tools for automated Planning and Scheduling systems.

Games with large branching factors pose a significant challenge for game tree search algorithms. In this paper, we address this problem with a sampling strategy for Monte Carlo Tree Search (MCTS) algorithms called "naive sampling", based on a variant of the Multi-armed Bandit problem called "Combinatorial Multi-armed Bandits" (CMAB). We analyze the theoretical properties of several variants of naive sampling, and empirically compare it against the other existing strategies in the literature for CMABs. We then evaluate these strategies in the context of real-time strategy (RTS) games, a genre of computer games characterized by their very large branching factors. Our results show that as the branching factor grows, naive sampling outperforms the other sampling strategies.

The way tech companies deal with online harassment and abuse is broken. YouTube allows anti-Semitism to stay live. Twitter waffles as targeted harassment runs rampant. Facebook takes down an iconic photo that shouldn't be banned. Now one German politician is tired of letting platforms make excuses.

Every day, more than 40 container ships pass through the Panama Canal. They chug over the narrow isthmus separating the Atlantic and Pacific oceans, navigating three sets of multi-chambered locks that carry them uphill to an enormous lake 90 feet above sea level. After crossing that, another network of locks lowers them to the opposite coast. The trip, which can take a full day depending on traffic, requires the careful choreography of skilled freighter pilots, tugboats, and the immense doors that separate each lock. As with most things these days, software keeps everything moving smoothly, but this most impressive feat of civil engineering relies upon a hodgepodge of systems added piecemeal over the decades.

This paper investigates three different technologies for solving a planning and scheduling problem of deploying multiple robots in a retirement home environment to assist elderly residents. The models proposed make use of standard techniques and solvers developed in AI planning and scheduling, with two primary motivations. First, to find a planning and scheduling solution that we can deploy in our real-world application. Second, to evaluate planning and scheduling technology in terms of the ``model-and-solve'' functionality that forms a major research goal in both domain-independent planning and constraint programming. Seven variations of our application are studied using the following three technologies: PDDL-based planning, time-line planning and scheduling, and constraint-based scheduling. The variations address specific aspects of the problem that we believe can impact the performance of the technologies while also representing reasonable abstractions of the real world application. We evaluate the capabilities of each technology and conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application. PDDL-based planning is able to find mostly low quality solutions while the timeline approach was unable to model the full problem without alterations to the solver code, thus moving away from the model-and-solve paradigm. It would be misleading to conclude that constraint programming is ``better'' than PDDL-based planning in a general sense, both because we have examined a single application and because the approaches make different assumptions about the knowledge one is allowed to embed in a model. Nonetheless, we believe our investigation is valuable for AI planning and scheduling researchers as it highlights these different modelling assumptions and provides insight into avenues for the application of AI planning and scheduling for similar robotics problems. In particular, as constraint programming has not been widely applied to robot planning and scheduling in the literature, our results suggest significant untapped potential in doing so.