Submodular Function Maximization for Group Elevator Scheduling

We propose a novel approach for group elevator scheduling by formulating it as the maximization of submodular function under a matroid constraint. In particular, we propose to model the total waiting time of passengers using a quadratic Boolean function. The unary and pairwise terms in the function denote the waiting time for single and pairwise allocation of passengers to elevators, respectively. We show that this objective function is submodular. The matroid constraints ensure that every passenger is allocated to exactly one elevator. We use a greedy algorithm to maximize the submodular objective function, and derive provable guarantees on the optimality of the solution. We tested our algorithm using Elevate 8, a commercial-grade elevator simulator that allows simulation with a wide range of elevator settings. We achieve significant improvement over the existing algorithms.Comment: 10 pages; 2017 International Conference on Automated Planning and Scheduling (ICAPS

Reinforcement Learning: A Survey

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file

Evolutionary Networks for Multi-Behavioural Robot Control : A thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science Massey University, Albany, New Zealand

Artificial Intelligence can be applied to a wide variety of real world problems, with varying levels of complexity; nonetheless, real world problems often demand for capabilities that are difficult, if not impossible to achieve using a single Artificial Intelligence algorithm. This challenge gave rise to the development of hybrid systems that put together a combination of complementary algorithms. Hybrid approaches come at a cost however, as they introduce additional complications for the developer, such as how the algorithms should interact and when the independent algorithms should be executed. This research introduces a new algorithm called Cascading Genetic Network Programming (CGNP), which contains significant changes to the original Genetic Network Programming. This new algorithm has the facility to include any Artificial Intelligence algorithm into its directed graph network, as either a judgement or processing node. CGNP introduces a novel ability for a scalable multiple layer network, of independent instances of the CGNP algorithm itself. This facilitates problem subdivision, independent optimisation of these underlying layers and the ability to develop varying levels of complexity, from individual motor control to high level dynamic role allocation systems. Mechanisms are incorporated to prevent the child networks from executing beyond their requirement, allowing the parent to maintain control. The ability to optimise any data within each node is added, allowing for general purpose node development and therefore allowing node reuse in a wide variety of applications without modification. The abilities of the Cascaded Genetic Network Programming algorithm are demonstrated and proved through the development of a multi-behavioural robot soccer goal keeper, as a testbed where an individual Artificial Intelligence system may not be sufficient. The overall role is subdivided into three components and individually optimised which allow the robot to pursue a target object or location, rotate towards a target and provide basic functionality for defending a goal. These three components are then used in a higher level network as independent nodes, to solve the overall multi- behavioural goal keeper. Experiments show that the resulting controller defends the goal with a success rate of 91%, after 12 hours training using a population of 400 and 60 generations

Genetic algorithm for controllers in elevator groups: analysis and simulation during lunchpeak traffic

The efficient performance of elevator group system controllers becomes a first order necessity when the buildings have a high utilisation ratio of the elevators, such as in professional buildings. We present a genetic algorithm that is compared with traditional controller algorithms in industry applications. An ARENA simulation scenario is created during heavy lunchpeak traffic conditions. The results allow us to affirm that our genetic algorithm reaches a better performance attending to the system waiting times than THV algorithm