Heuristic Exploration Research Articles

Exploring the Manifestation of Design for Manufacturing Heuristics in Students' Early-Stage Engineering Design Concepts

Abstract Additive manufacturing (AM) can produce designs in a manner that greatly differs from the methods used in the older, more familiar technologies of traditional manufacturing (TM). Despite this method of manufacturing designs that were seemingly inconceivable in the past, designers often forgo the new design considerations of AM and instead rely on their familiarity with the design considerations more commonly associated with TM, regardless of the manufacturing process they intend to use. This can lead to unnecessary material usage, increased manufacturing time, and can result in designs which are poorly manufactured. To remedy this problem, there is a need to intervene early in the design process to help address any concerns regarding the use of AM design heuristics. This work aims to address this opportunity through a preliminary exploration of the design heuristics that students naturally leverage when creating designs in the context of TM and AM. With the common design heuristics that students are most likely to use in their designs identified, future studies will cement early-stage design support tools that emphasize the significant heuristics found herein. For example, this work found that the design heuristic “incorporating complexity” was the most significant indicator of designs most suited for AM and should therefore be highly encouraged/emphasized when guiding designers in the use of AM. In doing so, it will be possible for early-stage design support tools to maximally improve designs that are intended to be manufactured for AM.

Malacca and Ming Dynasty in 15th-16th Centuries: A Diplomatic Relations and Regional Stability

<em>This study investigates the intricate relationship in diplomacy of Malacca with the Ming Dynasty of China during the 15th century in the Malacca Strait, with a focus on its impact on Diplomatic Relations. Employing a comprehensive historical methodology, including heuristic exploration, critical examination of sources, data interpretation, and historiographical analysis, the research reveals the significant consequences of mutually beneficial diplomatic engagements and trade interactions the bond from two region China and Malacca. When faced with threats came from Majapahit and Siam, Malacca found assistance from China (Ming Dynasty), resulting in the formation of a robust alliance. This diplomatic collaboration commenced with the arrival of Chinese delegated Yin C'ing in 1403 AD and was further reinforced by the influential visit of Cheng Ho in 1409 AD, solidifying the relationship between them. This mutually beneficial relationship not only safeguarded Malacca and elevated its trading stature but also facilitated China's access to valuable spices, while providing military protection from the Ming Dynasty. The deep relations Malacca had with China during the 15th century shows how their strengthened political association, connections and security also helps their cultural interactions.</em>

FGeo-DRL: Deductive Reasoning for Geometric Problems through Deep Reinforcement Learning

Human-like automatic deductive reasoning has always been one of the most challenging open problems in the interdisciplinary field of mathematics and artificial intelligence. This paper is the third in a series of our works. We built a neural-symbolic system, named FGeo-DRL, to automatically perform human-like geometric deductive reasoning. The neural part is an AI agent based on deep reinforcement learning, capable of autonomously learning problem-solving methods from the feedback of a formalized environment, without the need for human supervision. It leverages a pre-trained natural language model to establish a policy network for theorem selection and employ Monte Carlo Tree Search for heuristic exploration. The symbolic part is a reinforcement learning environment based on geometry formalization theory and FormalGeo, which models geometric problem solving (GPS) as a Markov Decision Process (MDP). In the formal symbolic system, the symmetry of plane geometric transformations ensures the uniqueness of geometric problems when converted into states. Finally, the known conditions and objectives of the problem form the state space, while the set of theorems forms the action space. Leveraging FGeo-DRL, we have achieved readable and verifiable automated solutions to geometric problems. Experiments conducted on the formalgeo7k dataset have achieved a problem-solving success rate of 86.40%.

Geochemical modeling of the mantle partial melting using heuristic exploration: an optimization model applied to earth sciences

Geochemical modeling of the mantle partial melting using heuristic exploration: an optimization model applied to earth sciences

NISQ-Ready Community Detection Based on Separation-Node Identification

The analysis of network structure is essential to many scientific areas ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO-based approach that only needs number-of-nodes qubits and is represented by a QUBO matrix as sparse as the input graph’s adjacency matrix. The substantial improvement in the sparsity of the QUBO matrix, which is typically very dense in related work, is achieved through the novel concept of separation nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which, upon its removal from the graph, yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept by achieving an up to 95% optimal solution quality on three established real-world benchmark datasets. This work hence displays a promising approach to NISQ-ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large-scale, real-world problem instances.

Leveraging heuristic client selection for enhanced secure federated submodel learning

As the number of clients for federated learning (FL) has expanded to the billion level, a new research branch named secure federated submodel learning (SFSL) has emerged. In SFSL, mobile clients only download a tiny ratio of the global model from the coordinator’s global. However, SFSL provides little guarantees on the convergence and accuracy performance as the covered items may be highly biased. In this work, we formulate the problem of client selection through optimizing unbiased coverage of item index set for enhancing SFSL performance. We analyze the NP-hardness of this problem and propose a novel heuristic multi-group client selection framework by jointly optimizing index diversity and similarity. Specifically, heuristic exploration on some random client groups are performed progressively for an empirical approximate solution. Meanwhile, private set operations are used to preserve the privacy of participated clients. We implement the proposal by simulating large-scale SFSL application in a lab environment and conduct evaluations on two real-world data-sets. The results demonstrate the performance (w.r.t., accuracy and convergence speed) superiority of our selection algorithm than SFSL. The proposal is also shown to yield significant computation advantage with similar communication performance as SFSL.

A new noise network and gradient parallelisation‐based asynchronous advantage actor‐critic algorithm

Asynchronous advantage actor-critic (A3C) algorithm is a commonly used policy optimization algorithm in reinforcement learning, in which asynchronous is parallel interactive sampling and training, and advantage is a sampling multi-step reward estimation method for computing weights. In order to address the problem of low efficiency and insufficient convergence caused by the traditional heuristic exploration of A3C algorithm in reinforcement learning, an improved A3C algorithm is proposed in this paper. In this algorithm, a noise network function, which updates the noise tensor in an explicit way is constructed to train the agent. Generalised advantage estimation (GAE) is also adopted to describe the dominance function. Finally, a new mean gradient parallelisation method is designed to update the parameters in both the primary and secondary networks by summing and averaging the gradients passed from all the sub-processes to the main process. Simulation experiments were conducted in a gym environment using the PyTorch Agent Net (PTAN) advanced reinforcement learning library, and the results show that the method enables the agent to complete the learning training faster and its convergence during the training process is better. The improved A3C algorithm has a better performance than the original algorithm, which can provide new ideas for subsequent research on reinforcement learning algorithms.

Architecture entropy sampling-based evolutionary neural architecture search and its application in osteoporosis diagnosis

In recent years, neural architecture search (NAS) has achieved unprecedented development because of its ability to automatically achieve high-performance neural networks in various tasks. Among these, the evolutionary neural architecture search (ENAS) has impressed the researchers due to the excellent heuristic exploration capability. However, the evolutionary algorithm-based NAS are prone to the loss of population diversity in the search process, causing that the structure of the surviving individuals is exceedingly similar, which will lead to premature convergence and fail to explore the search space comprehensively and effectively. To address this issue, we propose a novel indicator, named architecture entropy, which is used to measure the architecture diversity of population. Based on this indicator, an effective sampling strategy is proposed to select the candidate individuals with the potential to maintain the population diversity for environmental selection. In addition, an unified encoding scheme of topological structure and computing operation is designed to efficiently express the search space, and the corresponding population update strategies are suggested to promote the convergence. The experimental results on several image classification benchmark datasets CIFAR-10 and CIFAR-100 demonstrate the superiority of our proposed method over the state-of-the-art comparison ones. To further validate the effectiveness of our method in real applications, our proposed NAS method is applied in the identification of lumbar spine X-ray images for osteoporosis diagnosis, and can achieve a better performance than the commonly used methods. Our source codes are available at https://github.com/LabyrinthineLeo/AEMONAS.

Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning.

With the rapid development of unmanned combat aerial vehicle (UCAV)-related technologies, UCAVs are playing an increasingly important role in military operations. It has become an inevitable trend in the development of future air combat battlefields that UCAVs complete air combat tasks independently to acquire air superiority. In this paper, the UCAV maneuver decision problem in continuous action space is studied based on the deep reinforcement learning strategy optimization method. The UCAV platform model of continuous action space was established. Focusing on the problem of insufficient exploration ability of Ornstein–Uhlenbeck (OU) exploration strategy in the deep deterministic policy gradient (DDPG) algorithm, a heuristic DDPG algorithm was proposed by introducing heuristic exploration strategy, and then a UCAV air combat maneuver decision method based on a heuristic DDPG algorithm is proposed. The superior performance of the algorithm is verified by comparison with different algorithms in the test environment, and the effectiveness of the decision method is verified by simulation of air combat tasks with different difficulty and attack modes.

Hyper-Heuristic Firefly Algorithm Based Convolutional Neural Networks for Big Data Cyber Security

Objectives: A highly accurate Intrusion detection model is developed that classifies both the network-based and host-based intrusions without any complexity issues. Method: An optimized Deep Learning (DL) algorithm of IDS model is presented in the form of a Hyper-Heuristic Firefly Algorithm based Convolutional Neural Networks (HHFA-CNN). This proposed HHFACNN reduces false values and improves accuracy without increasing the complexities. Findings: The proposed HHFA-CNN system is performed on two network traffic datasets: NSL-KDD and ISCX-IDS. The outcomes demonstrated that the proposed HHFA-CNN model gives predominant execution than the other existing models. Novelty: The proposed model has employed a novel Hyper-Heuristic Firefly Algorithm for optimizing the hyper-parameters of the CNN. This model maintains the standard guidelines of the firefly algorithm and applies the high-level technique for controlling the exploration and determination of low-level heuristics. Keywords: Big data; Cyber security; Intrusion detection system; Hyper-Heuristic Firefly Algorithm; Convolutional Neural Networks

How Do You Play When You’re Prey?

Creativity is woven into the culture of Black America. Our histories and struggles as members of the minoritized African Diaspora are recorded and passed on in song and story, in movement and design. We are – and have been – the creators of an evolving culture that is simultaneously underestimated and desired by dominant culture. This othering poses real and pressing threats to our lives and livelihoods, as we are consumed and exploited to the point of erasure; and yet we keep creating. But why? What is creativity to the Black American living in such a predatory society? And how do I, as a Black creative minoritized in a Healing profession, engage with it? How do you play when you’re prey? These questions form the basis for an heuristic exploration into a video blog project entitled “Black Creative Healing,” where Black creatives are recorded engaging in conversation and collaboration over concepts relating to Blackness, Creativity, and the Healing process. Through arts-based analysis of past collaborations, available publicly on Youtube, I will investigate my own motivations, inspirations and roadblocks to the creative process as a Black healer. I will interrogate the directions and intentions laid bare by my creative endeavors and seek to define a central ethos by which other Black creatives may find themselves seen and encouraged, in the interest of finding balance between the “me” that is – and has been – prey, and the “me” that has only ever known – and been known by – play.

A Study of the Effects and Benefits of Custom-Precision Mathematical Libraries for HPC Codes

Mathematical libraries are typically developed for use with the fixed-width data-paths on processors and target common floating-point formats such as IEEE binary32 and binary64 . To address the increasing energy consumption and throughput requirements of HPC, scientific computing and AI applications, libraries and hardware implementations now provide new floating-point formats, allowing mathematical function evaluations with different performance and accuracy trade-offs. In this article we present a methodology and its associated proof-of-concept tool to evaluate the benefits of custom accuracy of mathematical library calls in HPC and scientific computations. First, our tool collects for each call-site of a mathematical function the input- and output-data profile. Then, using a heuristic exploration algorithm, we estimate the minimal required accuracy by rounding the result to lower precisions. The data profile and accuracy measurement per call-site is used to speculatively select the mathematical function implementation with the most appropriate accuracy for a given scenario. We have tested the methodology with the Intel MKL Vector Math ( VM ) library, leveraging the predefined accuracy levels. We demonstrate the benefits of our approach on two real-world applications: SGP4 , a satellite tracking application, and PATMOS , a Monte Carlo neutron transport code. The robustness of the methodology is estimated by measuring the numerical accuracy of the resulting optimized code, against user-defined criteria. We experiment and discuss generalization across data-sets and finally propose a speculative runtime implementation for PATMOS . The experiment provides an insight into the performance improvements achievable by leveraging the control of per-function call-site accuracy-mode execution of the Intel compiler SVML library. We show benefits from 13 to 55 percent in time reduction for the PATMOS use case.

A Qualitative Exploration of Heuristics and Cognitive Biases in Auditor Judgements

Professional judgement is inherent in financial statement audits because various methods, techniques, or approaches prescribed in auditing standards do not provide auditors with detailed guidance or specific audit criteria. While auditors are expected to exercise their judgements based on careful reasoning, there is a possibility that they do not always follow such an approach and instead make their judgements using heuristics. This study aims to penetrate and reveal whether there are cognitive biases in the judgements of auditors and what heuristics lead to these biases. This study employs a qualitative research design and uses ethnomethodology as a research approach. Data were collected using in-depth semi-structured interviews with 15 auditors who were either partners, managers, seniors, or juniors at a public accounting firm. Using the heuristic-bias framework as a theoretical lens and based on an analysis involving data condensation, data display, and conclusion drawing and verification, this study identifies five types of biases that auditors can experience: jumping to conclusions, groupthink, representativeness, availability, and anchoring biases. The results of this study present practical implications for auditors, accounting professional associations, public accounting firms, and academic institutions. That is, the findings provide insights for formulating strategies aimed at raising auditors’ awareness about possible systematic errors, or biases, in professional judgements when auditors rely on heuristics as a simplifying judgement-making strategy.

Fight the power: A heuristic exploration of systemic racism through dreams

AbstractSystemic racism, like many forms of difference and diversity, is poorly understood within the realms of counselling and psychotherapy. Whereas our profession has shied away from using its skills to explore the meaning and relevance of racism, this author over a number of years has studied many forms of intersectional otherness and recognises that racism is as much a relational experience between subject and other, both internally and externally. This paper therefore explores how an internalised experience of systemic racism can be understood through client work and dreamwork, offering ideas as to how the dreams of the racialised other may be understood; the symbolism, and the relevance of the racial complex hidden within them.

Robot path planning optimization method based on heuristic multi-directional rapidly-exploring tree

Aiming at the problems of low exploration efficiency and high optimal solution cost for existing robot path planning methods, a robot path planning optimization method based on heuristic multi-directional rapidly-exploring tree is implemented. In high-dimensional configuration space, drawing on the Rapid-exploration Random Tree (RRT) related algorithm idea, directional sampling control module works under the guidance of the robot goal course as a heuristic exploration. A flexible multi-directional rapidly-exploring tree construction method is used due to a degree of directional instability. In accordance with the principle of the centripetal growth of the tree, new multi-directional trees will be built on demand to arrive at specific coverage of the space. Then based on the previous path exploration vertices, through the merging method of the trees, a closed loop path is formed and optimized to finally generate a relative optimal path. Simulation experiment results show that this method could effectively improve the exploring efficiency with low computational cost.

Variational Bayesian Exploration-Based Active Sarsa Algorithm

We proposed an improved variational Bayesian exploration-based active Sarsa (VBE-ASAR) algorithm, which tries to balance the exploration and exploitation dilemma, and speeds up the convergence rate. First, in the learning process, variational Bayesian method is adopted to measure the information gain, which is used as an exploration factor to construct an internal reward function for heuristic exploration. In addition, before the learning process, in order to improve the exploration performance, transfer learning is used to initialize the value function, where Bisimulation metric is introduced to measure the distance between two states from the source MDP and the target MDP, respectively. Finally, we apply the proposed algorithm to the cliff walking problem, and compare with the Sarsa algorithm, the Q-Learning algorithm, the VFT-Sarsa algorithm and the Bayesian Sarsa (BS) algorithm. Experimental results show that the VBE-ASAR algorithm has a faster learning rate.

Decision-Making in Small Oil Businesses: An Exploration of Cognition, Heuristics, & Expertise

Decision-Making in Small Oil Businesses: An Exploration of Cognition, Heuristics, & Expertise

End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer

Nonprehensile rearrangement is the problem of controlling a robot to interact with objects through pushing actions in order to reconfigure the objects into a predefined goal pose. In this work, we rearrange one object at a time in an environment with obstacles using an end-to-end policy that maps raw pixels as visual input to control actions without any form of engineered feature extraction. To reduce the amount of training data that needs to be collected using a real robot, we propose a simulation-to-reality transfer approach. In the first step, we model the nonprehensile rearrangement task in simulation and use deep reinforcement learning to learn a suitable rearrangement policy, which requires in the order of hundreds of thousands of example actions for training. Thereafter, we collect a small dataset of only 70 episodes of real-world actions as supervised examples for adapting the learned rearrangement policy to real-world input data. In this process, we make use of newly proposed strategies for improving the reinforcement learning process, such as heuristic exploration and the curation of a balanced set of experiences. We evaluate our method in both simulation and real setting using a Baxter robot to show that the proposed approach can effectively improve the training process in simulation, as well as efficiently adapt the learned policy to the real world application, even when the camera pose is different from simulation. Additionally, we show that the learned system not only can provide adaptive behavior to handle unforeseen events during executions, such as distraction objects, sudden changes in positions of the objects, and obstacles, but also can deal with obstacle shapes that were not present in the training process.

Congestion pricing in a real-world oriented agent-based simulation context

This paper investigates optimal congestion pricing strategies using a real-world oriented agent-based simulation framework which allows for complex user behavior. The applied simulation approach accounts for iteratively learning transport users, stochastic demand, and only approximates the user equilibrium, which may be considered as closer to real-world than a model where transport users behave completely rational, have a perfect knowledge about all travel alternatives, and travel behavior strictly follows the user equilibrium. Two congestion pricing rules are developed and investigated. The first one directly builds on the Pigouvian taxation principle and computes marginal external congestion costs based on the queuing dynamics at the bottleneck links; resulting toll payments differ from agent to agent depending on the position in the queue (QCP approach). The second one uses control-theoretical elements to adjust toll levels depending on the congestion level in order to reduce or eliminate traffic congestion; resulting toll payments are the same for all travelers per time bin and road segment (LP approach). The pricing rules are applied to Vickrey's bottleneck model and the case study of the Greater Berlin area. The simulation experiments reveal that with and without mode and departure time choice, the rather simple LP rule results in a higher system welfare compared to the more complex QCP approach. The LP rule appears to better take into account the system's dynamics and the agents' learning behavior. The results also reveal that pricing significantly reduces traffic congestion, however, there is still a remaining delay, even with departure time choice. Overall, this paper points out further need for research and contributes to the exploration of optimization heuristics for real-world oriented simulation approaches.

Research on Air Confrontation Maneuver Decision-Making Method Based on Reinforcement Learning

With the development of information technology, the degree of intelligence in air confrontation is increasing, and the demand for automated intelligent decision-making systems is becoming more intense. Based on the characteristics of over-the-horizon air confrontation, this paper constructs a super-horizon air confrontation training environment, which includes aircraft model modeling, air confrontation scene design, enemy aircraft strategy design, and reward and punishment signal design. In order to improve the efficiency of the reinforcement learning algorithm for the exploration of strategy space, this paper proposes a heuristic Q-Network method that integrates expert experience, and uses expert experience as a heuristic signal to guide the search process. At the same time, heuristic exploration and random exploration are combined. Aiming at the over-the-horizon air confrontation maneuver decision problem, the heuristic Q-Network method is adopted to train the neural network model in the over-the-horizon air confrontation training environment. Through continuous interaction with the environment, self-learning of the air confrontation maneuver strategy is realized. The efficiency of the heuristic Q-Network method and effectiveness of the air confrontation maneuver strategy are verified by simulation experiments.