Dynamic Controllability (DC) of a Simple Temporal Problem with Uncertainty (STPU) uses a dynamic decision strategy, rather than a fixed schedule, to tackle temporal uncertainty. We extend this concept to the Controllable Conditional Temporal Problem with Uncertainty (CCTPU), which extends the STPU by conditioning temporal constraints on the assignment of controllable discrete variables. We define dynamic controllability of a CCTPU as the existence of a strategy that decides on both the values of discrete choice variables and the scheduling of controllable time points dynamically. This contrasts with previous work, which made a static assignment of choice variables and dynamic decisions over time points only. We propose an algorithm to find such a fully dynamic strategy. The algorithm computes the "envelope" of outcomes of temporal uncertainty in which a particular assignment of discrete variables is feasible, and aggregates these over all choices. When an aggregated envelope covers all uncertain situations of the CCTPU, the problem is dynamically controllable. However, the algorithm is complete only under certain assumptions. Experiments on an existing set of CCTPU benchmarks show that there are cases in which making both discrete and temporal decisions dynamically it is feasible to satisfy the problem constraints while assigning the discrete variables statically it is not.

Many planning applications involve complex relationships defined on high-dimensional, continuous variables. For example, robotic manipulation requires planning with kinematic, collision, visibility, and motion constraints involving robot configurations, object transforms, and robot trajectories. These constraints typically require specialized procedures to sample satisfying values. We extend the STRIPS planning language to support a generic, declarative specification for these procedures while treating their implementation as black boxes. We also describe cost-sensitive planning within this framework. We provide several domain-independent algorithms that reduce STRIPStream problems to a sequence of finite-domain STRIPS planning problems. Finally, we evaluate our algorithms on three robotic planning domains.

Real-world project scheduling often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simultaneous scheduling of multiple lots is mandatory in many production planning applications. To meet these requirements, a new flexible resource-constrained multi-project scheduling problem is introduced where both decisions (activity selection flexibility and time flexibility) are integrated. Besides the minimization of makespan, two alternative objectives inspired by a steel industry application case are presented: maximization of balanced length of selected activities (time balance) and maximization of balanced resource utilization (resource balance). New mixed integer and constraint programming (CP) models are proposed for the developed integrated flexible project scheduling problem. The real-world applicability of the suggested CP models is shown by solving large steel industry instances with the CP Optimizer of IBM ILOG CPLEX. Furthermore, benchmark instances on flexible resource-constrained project scheduling problems (RCPSP) are solved to optimality.

In real-world project scheduling applications, activity durations are often uncertain. Proactive scheduling can effectively cope with the duration uncertainties, by generating robust baseline solutions according to a priori stochastic knowledge. However, most of the existing proactive approaches assume that the duration uncertainty of an activity is not related to its scheduled start time, which may not hold in many real-world scenarios. In this paper, we relax this assumption by allowing the duration uncertainty to be time-dependent, which is caused by the uncertainty of whether the activity can be executed on each time slot. We propose a stochastic optimization model to find an optimal Partial-order Schedule (POS) that minimizes the expected makespan. This model can cover both the time-dependent uncertainty studied in this paper and the traditional time-independent duration uncertainty. To circumvent the underlying complexity in evaluating a given solution, we approximate the stochastic optimization model based on Sample Average Approximation (SAA). Finally, we design two efficient branch-and-bound algorithms to solve the NP-hard SAA problem. Empirical evaluation confirms that our approach can generate high-quality proactive solutions for a variety of uncertainty distributions.

Love it or hate it, technology is well and truly embedded into our lives. Investing in new tech isn't an option anymore, it's a necessity – especially when it comes to the workforce. A recent report from Brandon Hall Group found that 41% of employers want a better system of reporting on HR data, whilst 45% want to be able to alleviate the burden of manual tasks from HR's shoulders. With that in mind – it's time for practitioners to start embracing these digital changes, rather than shying away from them in abject fear. We spoke to Robert Childs, EVP HR Advisory Services & Capabilities at American Express, who revealed what new tools he'll be focusing on in 2019.

Chicago O'Hare International Airport had the highest number of flight disruptions Wednesday morning as a massive winter storm made its way across the U.S. It will bring snow, rain and an "icy mix" to parts of the Midwest and East on Wednesday evening. As of Wednesday morning, more than 2,000 flights in the Midwest and Northeast had been canceled, and another 3,000 delayed because of the storm, according to FlightAware. The flight-tracking website showed Chicago O'Hare as the worst place for disruptions between 7 and 11 a.m. CST, with 51 flights canceled and 163 delayed. Baltimore-Washington, Reagan National and Dulles airports in the Washington area also showed significant flight problems, with a combined total of 84 flights canceled and 51 delayed as of Wednesday morning.

If your workforce management leaves a lot to be desired, chances are good you're using an older platform that may not be reflecting today's contact center or help desk workforce reality. While companies are still looking to provide the most accurate information in the right channels at the right time, the way to do it has changed in recent years thanks to digital transformation (as have customer expectations), so it makes sense that the workforce management solution needs to evolve to keep up with the digital workplace. What is "Digital Transformation" in WFM? While the term gets used a lot, digital transformation will mean different things to different enterprise functions. Customers are increasingly looking for helps and answers on social platforms like Facebook (News - Alert) and Twitter, which means that agents are spending more time in these apps.

Timeline-based planning is an approach originally developed in the context of space mission planning and scheduling, where problem domains are modelled as systems made of a number of independent but interacting components, whose behaviour over time, the timelines, is governed by a set of temporal constraints. This approach is different from the action-based perspective of common PDDL-like planning languages. Timeline-based systems have been successfully deployed in a number of space missions and other domains. However, despite this practical success, a thorough theoretical understanding of the paradigm was missing. This thesis fills this gap, providing the first detailed account of formal and computational properties of the timeline-based approach to planning. In particular, we show that a particularly restricted variant of the formalism is already expressive enough to compactly capture action-based temporal planning problems. Then, finding a solution plan for a timeline-based planning problem is proved to be EXPSPACE-complete. Then, we study the problem of timeline-based planning with uncertainty, that include external components whose behaviour is not under the control of the planned system. We identify a few issues in the state-of-the-art approach based on flexible plans, proposing timeline-based games, a more general game-theoretic formulation of the problem, that addresses those issues. We show that winning strategies for such games can be found in doubly-exponential time. Then, we study the expressiveness of the formalism from a logic point of view, showing that (most of) timeline-based planning problems can be captured by Bounded TPTL with Past, a fragment of TPTL+P that, unlike the latter, keeps an EXPSPACE satisfiability problem. The logic is introduced and its satisfiabilty problem is solved by extending a recent one-pass tree-shaped tableau method for LTL.

This technical report documents the winner of the Computational Intelligence in Games(CIG) 2018 Hanabi competition. We introduce Re-determinizing IS-MCTS, a novel extension of Information Set Monte Carlo Tree Search (IS-MCTS) \cite{IS-MCTS} that prevents a leakage of hidden information into opponent models that can occur in IS-MCTS, and is particularly severe in Hanabi. Re-determinizing IS-MCTS scores higher in Hanabi for 2-4 players than previously published work. Given the 40ms competition time limit per move we use a learned evaluation function to estimate leaf node values and avoid full simulations during MCTS. For the Mixed track competition, in which the identity of the other players is unknown, a simple Bayesian opponent model is used that is updated as each game proceeds.

Inspired by the success of AlphaGo Zero (AGZ) which utilizes Monte Carlo Tree Search (MCTS) with Supervised Learning via Neural Network to learn the optimal policy and value function, in this work, we focus on establishing formally that such an approach indeed finds optimal policy asymptotically, as well as establishing non-asymptotic guarantees in the process. We shall focus on infinite-horizon discounted Markov Decision Process to establish the results. To start with, it requires establishing the MCTS's claimed property in the literature that for any given query state, MCTS provides approximate value function for the state with enough simulation steps of MDP. We provide non-asymptotic analysis establishing this property by analyzing a non-stationary multi-arm bandit setup. Our proof suggests that MCTS needs to be utilized with polynomial rather than logarithmic "upper confidence bound" for establishing its desired performance -- interestingly enough, AGZ chooses such polynomial bound. Using this as a building block, combined with nearest neighbor supervised learning, we argue that MCTS acts as a "policy improvement" operator; it has a natural "bootstrapping" property to iteratively improve value function approximation for all states, due to combining with supervised learning, despite evaluating at only finitely many states. In effect, we establish that to learn $\varepsilon$ approximation of value function in $\ell_\infty$ norm, MCTS combined with nearest-neighbors requires samples scaling as $\widetilde{O}\big(\varepsilon^{-(d+4)}\big)$, where $d$ is the dimension of the state space. This is nearly optimal due to a minimax lower bound of $\widetilde{\Omega}\big(\varepsilon^{-(d+2)}\big).$