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.

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.

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.

In this paper we look into the problem of planning over hybrid domains, where change can be both discrete and instantaneous, or continuous over time. In addition, it is required that each state on the trajectory induced by the execution of plans complies with a given set of global constraints. We approach the computation of plans for such domains as the problem of searching over a deterministic state model. In this model, some of the successor states are obtained by solving numerically the so-called initial value problem over a set of ordinary differential equations (ODE) given by the current plan prefix. These equations hold over time intervals whose duration is determined dynamically, according to whether zero crossing events take place for a set of invariant conditions. The resulting planner, FS+, incorporates these features together with effective heuristic guidance. FS+ does not impose any of the syntactic restrictions on process effects often found on the existing literature on Hybrid Planning. A key concept of our approach is that a clear separation is struck between planning and simulation time steps. The former is the time allowed to observe the evolution of a given dynamical system before committing to a future course of action, whilst the later is part of the model of the environment. FS+ is shown to be a robust planner over a diverse set of hybrid domains, taken from the existing literature on hybrid planning and systems.

Utrip, a Seattle startup that uses machine learning to help travelers plan their trips, just closed a $4 million funding round. Investors in the Series A round include Plug and Play, Tiempo Capital, Acorn Ventures, and executives from companies such as Apple and Costco, participating as angel investors. The cash will go toward Utrip's machine learning and data science operations, which fuel the platform's recommendation engine. "One of the things that our travelers love about Utrip is the depth with which we curate destinations and go beyond those top 10 lists that are available everywhere to offer experiences that are really unique and local and authentic for that destination," said Utrip CEO Gilad Berenstein. "That's one big priority, continuing to build out our machine learning capabilities as well as our human expert network, our chefs, artists, historians, etcetera."

In recent years, there has been growing interest in the study of automated playlist generation — music recommender systems that focus on modeling preferences over song sequences rather than on individual songs in isolation. This paper addresses this problem by learning personalized models on the fly of both song and transition preferences, uniquely tailored to each user’s musical tastes. Playlist recommender systems typically include two main components: i) a preference-learning component, and ii) a planning component for selecting the next song in the playlist sequence. While there has been much work on the former, very little work has been devoted to the latter. This paper bridges this gap by focusing on the planning aspect of playlist generation within the context of DJ-MC, our playlist recommendation application. This paper also introduces a new variant of playlist recommendation, which incorporates the notion of diversity and novelty directly into the reward model. We empirically demonstrate that the proposed planning approach significantly improves performance compared to the DJ-MC baseline in two playlist recommendation settings, increasing the usability of the framework in real world settings.