In this paper, the multiple operating room (OR) surgical case sequencing problem (SCSP) is addressed. The objective is to maximise total OR utilisation during standard opening hours. The work here is based on a case study of a large Australian public hospital with long surgical waiting lists and high levels of non-elective demand. Due to the complexity of the SCSP and the size of the instances considered herein, heuristic techniques are required to solve the problem. Constructive heuristics are presented based on both a modified block scheduling policy and an open scheduling policy. A number of real-time reactive strategies are presented that can be used to maintain schedule feasibility in the case of disruptions. Results of computational experiments show that the approach presented in this paper can be used to maintain schedule feasibility in real-time, whilst increasing OT utilisation and throughput, and reducing the waiting time of non-elective patients. The framework presented here is applicable to the real-life scheduling of OT departments, and recommendations have been provided regarding implementation of the approach.

In this paper we consider the surgical case sequencing problem (SCSP) under stochastic conditions. In addition to implementing a robust surgical schedule, we investigate the use of a number of reactive strategies that can be used to maintain schedule feasibility. We present a mixed integer nonlinear programming (MINLP) model for the reactive multiple operating room (OR) SCSP that may be suitable for direct implementation on small problem instances. A machine scheduling perspective is considered and the model is equivalent to a resource-constrained parallel-machine scheduling problem with identical machines, machine eligibility restrictions, and machine and job release dates. The explicit objective of the model is to reduce OR idle time, although other common objectives (including time to surgery and overtime) are discussed. The work here is based on a case study of a large Australian public hospital with long surgical waiting lists and high non-elective demand. Results of computational experiments show that the reactive strategies presented in this paper can be used to reduce idle time without putting excessive pressure on surgeons.

Finite State Controllers (FSCs) are an effective way to compactly represent sequential plans. By imposing appropriate conditions on transitions, FSCs can also represent generalized plans (plans that solve a range of planning problems from a given domain). In this paper we introduce the concept of hierarchical FSCs for planning by allowing controllers to call other controllers. This call mechanism allows hierarchical FSCs to represent generalized plans more compactly than individual FSCs, to compute controllers in a modular fashion or even more, to compute recursive controllers. The paper introduces a classical planning compilation for computing hierarchical FSCs that solve challenging generalized planning tasks. The compilation takes as input a finite set of classical planning problems from a given domain. The output of the compilation is a single classical planning problem whose solution induces: (1) a hierarchical FSC and (2), the corresponding validation of that controller on the input classical planning problems.

Abstract--This work proposes an engine for the Creation Of Novel Adventure Narrative (CONAN), which is a procedural quest generator. It uses a planning approach to story generation. The engine is tested on its ability to create quests, which are sets of actions that must be performed in order to achieve a certain goal, usually for a reward. The engine takes in a world description represented as a set of facts, including characters, locations, and items, and generates quests according to the state of the world and the preferences of the characters. We evaluate quests through the classification of the motivations behind the quests, based on the sequences of actions required to complete the quests. We also compare different world descriptions and analyze the difference in motivations for the quests produced by the engine. Compared against human structural quest analysis, the current engine was found to be able to replicate the quest structures found in commercial video game quests. The creation of media content has always been the domain of humans, be it for movies, music or video games. With advancement in computer technology and research, the creation of such content has seen a slight shift from the human authored to automatic computer generation. Using algorithms to procedurally create media can effectively alleviate some of the burden from artists when creating a new piece. A. Procedural Generation in Games Procedural Content Generation for Games (PCG-G) is the use of computers algorithms to generate game content, determine if it is interesting, and select the best ones on behalf of the players.[1] This type of generation becomes quite useful when trying to produce content for an industry that is more and more demanding in terms of content [1]. For instance, in the current market, game development costs are extremely high as the demand for highly complex games requires the work of many artists and many hours to be met. For instance, the Massively Multiplayer Online Role Playing Game (MMORPG) World of Warcraft has a total of 30,000 items, 5,300 creatures with which to interact and 7600 quests and has an estimated budget of twenty to one hundred and fifty million dollars for a single game [1].

Researchers often try to capture as much information as they can, either by using existing architectures, creating new ones, going deeper, or employing different training methods. This paper compares different ideas and methods that are used heavily in Machine Learning to determine what works best. These methods are prevalent in various domains of Machine Learning, such as Computer Vision and Natural Language Processing (NLP). Throughout our work, we have tried to bring generalization into context, because that's what matters in the end. Any model should be robust and able to work outside your research environment. When a model lacks generalization, very often we try to train the model on datasets it has never encountered … and that's when things start to get much more complex.

The basic argument is increasingly deployed by frustrated executives and self-promoting private-equity groups: Companies are doing dumb things to meet the market's quarterly expectations, and hurting their long-term prospects as a result. Take the company private and executives no longer have to care about the short term, allowing them to invest for the long run and help the company, their loyal shareholders and wider society. The trouble is that none of this applies to Tesla. It is hard to think of a company that cares less about sucking up to Wall Street than Tesla. Mr. Musk earlier this year rejected "boring bonehead questions" from analysts on his quarterly earnings call; the company offers no guidance on quarterly earnings; and it has frequently and unapologetically reported losses far worse than expected (only twice has it made a quarterly profit, both times a surprise).

Abstract-- Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven V ehicle (HV) and an Autonomous V ehicle (A V) drive together . We propose a planning framework where the degree to which the A V considers the other agent's reward is controlled by a selfishness factor . We test our approach on a simulated two-lane highway where the A V and HV merge into each other's lanes. In a user study with 21 subjects and 6 different selfishness factors, we found that our planning approach was sound and that both agents had less merging times when a factor that balances the rewards for the two agents was chosen. Our results on double lane merging suggest it to be a nonzero-sum game and encourage further investigation on collaborative decision making algorithms for mixed-autonomy traffic. Driving is a social activity: drivers indicate their willingness to change lanes by subtle cues such as eye contact, or by not-so-subtle cues such as adjusting their speed and position [1]. There has been impressive demonstrations of Autonomous V ehicle (A V) technology [2]-[4], however one of the remaining challenges in this area is reading those cues to estimate the intentions of other agents as well as using cues to communicate the intentions of the A V . As A Vs become commonplace, the situations where A V's and Human-driven V ehicles (HV) interact will increase.

The interview is one of the most fundamental aspects of an organization's hiring process, but getting those appointments on everyone's calendars can be a logistical nightmare. And relying on a tedious manual system may leave candidates with a bad first impression. "People don't have their calendars up-to-date, or they cancel and reschedule constantly," says Lin Lin Phan, talent operations manager at MuleSoft, a San Francisco-based technology firm. "Things happen, and we're the ones who have to step in and find a replacement interviewer before it has a negative effect on the candidate's experience." Fortunately, technology can help with automation tools that streamline the scheduling process.

Linear Squared, a Sri Lankan company offering Machine Learning and Advanced Data Analytics solutions, has launched a fully automated planning platform for apparel industry. The company claims the solution, Capacity Squared, to be the world's first AI driven production planning software. The process of capacity planning on a shopfloor has always been manual, which consumes more time and is prone to human errors and biases. In unforeseeable situations like delay of raw materials, missed targets etc., sometimes even the well-planned schedule runs on low efficiency. Thus, the solution lies in the optimisation of the planning schedule without expanding the factory by adding new machinery or hiring labour.

In Reinforcement Learning, the agents take random decisions in their environment and learns on selecting the right one out of many to achieve their goal and play at a super-human level. Policy and Value Networks are used together in algorithms like Monte Carlo Tree Search to perform Reinforcement Learning. Both the networks are an integral part of a method called Exploration in MCTS algorithm. They are also known as policy iteration & value iteration since they are calculated many times making it an iterative process. Let's understand why are they so important in Machine Learning and what's the difference between them?