Complexity cores and hard problem instances

Despite some success of Genetic Algorithms (GAs) when tackling Constraint Satisfaction Problems (CSPs), they generally suffer from poor crossover operators. In order to overcome this limitation in practice, we propose a novel crossover specifically designed for solving CSPs including Temporal CSPs (TCSPs). Together with a variable ordering heuristic and an integration into a parallel architecture, this proposed crossover enables the solving of large and hard problem instances as demonstrated by the experimental tests conducted on randomly generated CSPs and TCSPs based on the model RB. We will indeed demonstrate, through these tests, that our proposed method is superior to the known GA-based techniques for CSPs. In addition, we will show that we are able to compete with the efficient MAC-based Abscon 109 solver for random problem instances as well as those instances taken from Lecoutre’s CSP library. Finally, we conducted additional tests on very large consistent and over constrained CSPs and TCSPs instances in order to show the ability of our method to deal with constraint problems in real time. This corresponds to solving the CSP or the TCSP by giving a solution with a quality (number of solved constraints) depending on the time allocated for computation.

Download Full-text

The Hard Problem of Consciousness and John McDowell’s Epistemological Disjunctivism

Вопросы философии ◽

10.31857/s004287440007357-7 ◽

2019 ◽

Keyword(s):

Hard Problem ◽

Epistemological Disjunctivism ◽

The Hard Problem

Download Full-text

Zombies and Dolls

10.1093/oso/9780198728443.003.0002 ◽

2018 ◽

Author(s):

Marcello Massimini ◽

Giulio Tononi

Keyword(s):

Body Problem ◽

Thought Experiments ◽

Turing Test ◽

Hard Problem ◽

Mind Body Problem ◽

The Hard Problem ◽

The Mind ◽

The Brain

This chapter uses thought experiments and practical examples to introduce, in a very accessible way, the hard problem of consciousness. Soon, machines may behave like us to pass the Turing test and scientists may succeed in copying and simulating the inner workings of the brain. Will all this take us any closer to solving the mysteries of consciousness? The reader is taken to meet different kind of zombies, the philosophical, the digital, and the inner ones, to understand why many, scientists and philosophers alike, doubt that the mind–body problem will ever be solved.

Download Full-text

Decision Processes and Value Endogeneity

10.1093/oso/9780198825067.003.0004 ◽

2018 ◽

Author(s):

James G. March

Keyword(s):

Decision Processes ◽

Hard Problem ◽

Trade Offs ◽

Definition Of ◽

Over Time ◽

What Matters

Humans use reasons to shape and justify choices. In the process, trade-offs seem essential and often inevitable. But trade-offs involve comparisons, which are problematic both across values and especially over time. Reducing disparate values to a common metric (especially if that metric is money) is often problematic and unsatisfactory. Critically, it is not that values just shape choices, but that choices themselves shape values. This endogeneity of values makes an unconditional normative endorsement of modern decision-theoretic rationality unwise. This is a hard problem and there is no escaping the definition of good values, that is, those that make humans better. This removes the wall between economics and philosophy. If we are to adopt and enact this perspective, then greater discourse and debate on what matters and not just what counts will be useful and even indispensable.

Download Full-text

Using Machine Learning for Quantum Annealing Accuracy Prediction

Algorithms ◽

10.3390/a14060187 ◽

2021 ◽

Vol 14 (6) ◽

pp. 187

Author(s):

Aaron Barbosa ◽

Elijah Pelofske ◽

Georg Hahn ◽

Hristo N. Djidjev

Keyword(s):

Machine Learning ◽

Maximum Clique ◽

Classification Model ◽

Maximum Clique Problem ◽

Problem Instance ◽

Np Hard ◽

Machine Learning Classification ◽

Hard Problems ◽

Problem Instances ◽

D Wave

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

Download Full-text

Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽

10.3390/healthcare9020126 ◽

2021 ◽

Vol 9 (2) ◽

pp. 126

Author(s):

Hai-Feng Ling ◽

Zheng-Lian Su ◽

Xun-Lin Jiang ◽

Yu-Jun Zheng

Keyword(s):

Prevention And Control ◽

Large Scale ◽

Medical Services ◽

The Novel ◽

Multi Objective ◽

Medical Supplies ◽

Problem Instances ◽

Novel Coronavirus ◽

And Control ◽

Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

Download Full-text

Zero Queueing for Multi-Server Jobs

Proceedings of the ACM on Measurement and Analysis of Computing Systems ◽

10.1145/3447385 ◽

2021 ◽

Vol 5 (1) ◽

pp. 1-25

Author(s):

Weina Wang ◽

Qiaomin Xie ◽

Mor Harchol-Balter

Keyword(s):

Cloud Computing ◽

Stability Region ◽

Transient Analysis ◽

Queueing Systems ◽

Sufficient Conditions ◽

Hard Problem ◽

Queueing Model ◽

Multiple Servers ◽

First Results ◽

Multi Server

Cloud computing today is dominated by multi-server jobs. These are jobs that request multiple servers simultaneously and hold onto all of these servers for the duration of the job. Multi-server jobs add a lot of complexity to the traditional one-server-per-job model: an arrival might not "fit'' into the available servers and might have to queue, blocking later arrivals and leaving servers idle. From a queueing perspective, almost nothing is understood about multi-server job queueing systems; even understanding the exact stability region is a very hard problem. In this paper, we investigate a multi-server job queueing model under scaling regimes where the number of servers in the system grows. Specifically, we consider a system with multiple classes of jobs, where jobs from different classes can request different numbers of servers and have different service time distributions, and jobs are served in first-come-first-served order. The multi-server job model opens up new scaling regimes where both the number of servers that a job needs and the system load scale with the total number of servers. Within these scaling regimes, we derive the first results on stability, queueing probability, and the transient analysis of the number of jobs in the system for each class. In particular we derive sufficient conditions for zero queueing. Our analysis introduces a novel way of extracting information from the Lyapunov drift, which can be applicable to a broader scope of problems in queueing systems.

Download Full-text