Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches——A Systematic Literature Review and Mapping Study

The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed.While AI is a powerfulmeans to discover interesting patterns and obtain predictive models,the use of these algorithms comes with a great responsibility,as an incomplete or unbalanced set of training data or an unproper interpretation of the models’outcomes could result in misleading conclusions that ultimately could become very dangerous.For these reasons,it is important to rely on expert knowledge when applying these methods.However,not every user can count on this specific expertise;non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems,but they need basic guidelines to obtain themost out of AI models.The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features.The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering.As a result,9 papers that tackle AI algorithmrecommendation through tangible and traceable rules and heuristics were collected.The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.

Exact and heuristic formulae to compute the geodetic height from the ellipse equation

The conversion of the cartesian coordinates of a point to its geodetic equivalent coordinates in reference to the geodetic ellipsoid is one of the main challenges in geodesy.The ellipse equation in the meridian plane significantly influences the value of the geodetic coordinates.This research analyzes this influence and how it can contribute to their solutions.The study investigates the mathematical relation between them and presents an exact formula relating to the geodetic height and the ellipse equation.In addition,a heuristic formula for the relation between the geodetic height and the ellipse equation is proposed,which is independent of the geodetic latitude and has a relative accuracy better than 99.9 %.The calculation is stable,and the cost is low.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Analyzing and De-Anonymizing Bitcoin Networks:An IP Matching Method with Clustering and Heuristics

The anonymity and de-anonymity of blockchain and Bitcoin have always been a hot topic in blockchain related research.Since Bitcoin was created by Nakamoto in 2009,it has,to some extent,deviated from its currency attribute as a trading medium but instead turned into an object for financial investment and operations.In this paper,the power-law distribution that the Bitcoin network obeys is given with mathematical proof,while traditional deanonymous methods such as clustering fail to satisfy it.Therefore,considering the profit-oriented characteristics of Bitcoin traders in such occasion,we put forward a de-anonymous heuristic approach that recognizes and analyzes the behavioral patterns of financial High-Frequency Transactions(HFT),with realtime exchange rate of Bitcoin involved.With heuristic approach used for de-anonymity,algorithm that deals with the adjacency matrix and transition probability matrix are also put forward,which then makes it possible to apply clustering to the IP matching method.Basing on the heuristic approach and additional algorithm for clustering,finally we established the de-anonymous method that matches the activity information of the IP with the transaction records in blockchain.Experiments on IP matching method are applied to the actual data.It turns out that similar behavioral pattern between IP and transaction records are shown,which indicates the superiority of IP matching method.

A Hybrid Heuristics for Irregular Flight Recovery

Adverse weather conditions,congestion at airports,and mechanical failures often disrupt regular flight schedules. The irregular flight recovery problem aims to recover these schedules through reassignments of flights and cancellations. In this article,we develop the classic resource assignment model for the irregular flight recovery problem,and a new hybrid heuristic procedure based on greedy random adaptive search procedure （GRASP） and simulated annealing algorithm is presented to solve this problem. As compared with the original GRASP method,the proposed algorithm demonstrates quite a high global optimization capability. Computational experiments on large-scale problems show that the proposed procedure is able to generate feasible revised flight schedules of good quality in less than five seconds.

Research on Different Heuristics for Minimax Algorithm Insight from Connect-4 Game

Minimax algorithm and machine learning technologies have been studied for decades to reach an ideal optimization in game areas such as chess and backgammon. In these fields, several generations try to optimize the code for pruning and effectiveness of evaluation function. Thus, there are well-armed algorithms to deal with various sophisticated situations in gaming occasion. However, as a traditional zero-sum game, Connect-4 receives less attention compared with the other members of its zero-sum family using traditional minimax algorithm. In recent years, new generation of heuristics is created to address this problem based on research conclusions, expertise and gaming experiences. However, this paper mainly introduced a self-developed heuristics supported by well-demonstrated result from researches and our own experiences which fighting against the available version of Connect-4 system online. While most previous works focused on winning algorithms and knowledge based approaches, we complement these works with analysis of heuristics. We have conducted three experiments on the relationship among functionality, depth of searching and number of features and doing contrastive test with sample online. Different from the sample based on summarized experience and generalized features, our heuristics have a basic concentration on detailed connection between pieces on board. By analysing the winning percentages when our version fights against the online sample with different searching depths, we find that our heuristics with minimax algorithm is perfect on the early stages of the zero-sum game playing. Because some nodes in the game tree have no influence on the final decision of minimax algorithm, we use alpha-beta pruning to decrease the number of meaningless node which greatly increases the minimax efficiency. During the contrastive experiment with the online sample, this paper also verifies basic characters of the minimax algorithm including depths and quantity of features. According to the experiment, these two characters can both effect the decision for each step and none of them can be absolutely in charge. Besides, we also explore some potential future issues in Connect-4 game optimization such as precise adjustment on heuristic values and inefficiency pruning on the search tree.

A new heuristics model of simulating pedestrian dynamics based on Voronoi diagram

A new heuristics model based on the Voronoi diagram is presented to simulate pedestrian dynamics with the noncrowded state, in which these mechanisms of preference demand evading and surpassing, microscopic anti-deadlock, and site-fine-tuning are considered. The preference demand describes the willingness determination of detouring or following other pedestrians. In the evading and surpassing mechanisms, in order to achieve a balance between avoiding conflicts and minimizing detour distances, a new pair of concepts: "allow-areas and denial-areas" are introduced to divide the feasible region for pedestrians detour behaviors, in which the direction and magnitude of detour velocity are determined.A microscopic anti-deadlock mechanism is inserted to avoid deadlock problem of the counter-directional pedestrian. A site-fine-tuning mechanism is introduced to describe the behavior of avoiding getting too close to the neighbors in pedestrian movement. The presented model is verified through multiple scenarios, including the uni-or bi-direction pedestrian flow in the corridor without obstacles, the uni-direction pedestrian flow in the corridor with obstacles, and the pedestrian evacuation from a room with single-exit. The simulation results show that the velocity–density relationship is consistent with empirical data. Some self-organizing phenomena, such as lanes formation and arching are observed in the simulation.When pedestrians detour an obstacle, the avoiding area before the obstacle and the unoccupied area after the obstacle can be observed. When pedestrians evacuate through a bottleneck without panic, the fan-shaped crowd can be found, which is consistent with the actual observation. It is also found that the behavior of following others in an orderly manner is more conducive to the improvement of the overall movement efficiency when the crowd moves in a limited space.

Flower Pollination Heuristics for Nonlinear Active Noise Control Systems

In this paper,a novel design of the flower pollination algorithm is presented for model identification problems in nonlinear active noise control systems.The recently introduced flower pollination based heuristics is implemented to minimize the mean squared error based merit/cost function representing the scenarios of active noise control system with linear/nonlinear and primary/secondary paths based on the sinusoidal signal,random and complex random signals as noise interferences.The flower pollination heuristics based active noise controllers are formulated through exploitation of nonlinear filtering with Volterra series.The comparative study on statistical observations in terms of accuracy,convergence and complexity measures demonstrates that the proposed meta-heuristic of flower pollination algorithm is reliable,accurate,stable as well as robust for active noise control system.The accuracy of the proposed nature inspired computing of flower pollination is in good agreement with the state of the art counterpart solvers based on variants of genetic algorithms,particle swarm optimization,backtracking search optimization algorithm,fireworks optimization algorithm along with their memetic combination with local search methodologies.Moreover,the central tendency and variation based statistical indices further validate the consistency and reliability of the proposed scheme mimic the mathematical model for the process of flower pollination systems.

A Heuristics-Based Cost Model for Scientic Workow Scheduling in Clou

Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.

Layer-layout-based heuristics for loading homogeneous items into a single container

The container loading problem (CLP) is a well-known NP-hard problem. Due to the computation complexity, heuristics is an often-sought approach. This article proposes two heuristics to pack homogeneous rectangular boxes into a single container. Both algorithms adopt the concept of building layers on one face of the container, but the first heuristic determines the layer face once for all, while the second treats the remaining container space as a reduced-sized container after one layer is loaded and, hence, selects the layer face dynamically. To handle the layout design problem at a layer's level, a block-based 2D packing procedure is also developed. Numerical studies demonstrate the efficiency of the heuristics.

Combined heuristics for determining order quantity under time-varying demands

The time-varying demands for a certain period are often assumed to be less than the basic economic order quantity （EOQ） so that total replenishment quantity rather than economic order quantity is normally considered by most of the heuristics. This acticle focuses on a combined heuristics method for determining order quantity under generalized time-varying demands. The independent policy （IP）, abnormal independent policy （AIP） and dependent policies are studied and compared. Using the concepts of normal/abnormal periods and the properties of dependent policies, a dependent policy-based heuristics （DPH） is proposed for solving the order quantity problems with a kind of time-varying demands pattern under which the first period is normal. By merging the Silver-Meal （S-M） heuristics and the dependent policy-based heuristics （DPH）, a combined heuristics （DPH/S-M） is developed for solving order quantity problems with generalized time-varying demands. The experimentation shows that （1） for the problem with one normal period, no matter which position the normal period stands, the DPH/S-M could not guarantee better than the S-M heuristics, however it is superior to the S-M heuristics in the case that the demands in the abnormal periods are in descending order, and （2） The DPH/S-M is superior to the S-M heuristics for problems with more than one normal period, and the more the number of normal periods, the greater the improvements.

Scheduling Heuristics for Live Video Transcoding on Cloud Edges

Efficient video delivery involves the transcoding of the original sequence into various resolutions,bitrates and standards,in order to match viewers’capabilities.Since video coding and transcoding are computationally demanding,performing a portion of these tasks at the network edges promises to decrease both the workload and network traffic towards the data centers of media providers.Motivated by the increasing popularity of live casting on social media platforms,in this paper we focus on the case of live video transcoding.Specifically,we investigate scheduling heuristics that decide on which jobs should be assigned to an edge minidatacenter and which to a backend datacenter.Through simulation experiments with different Qo S requirements we conclude on the best alternative.

Heuristics Scheduling Algorithm on the Links Scheduling of Satellite-Ground Clock Synchronization and Ephemeris Uploading in COMPASS System

The execution process of satellite-ground clock synchronization and ephemeris uploading in the system is analyzed,as well as their characterized operation and their relationship.Based on the analysis of the scheduling goal and constraint character,a heuristics rule-based multi-stage link scheduling algorithm was put forward.The algorithm distinguishes the on-off-frontier satellites from the others and schedules them by turns.The paper presented the main flow as well as the detailed design of the rule.Finally based on the current COMPASS global system,some typical resources and constraints are selected to generate an instance.Then the comparison analysis between the heuristics scheduling algorithm and three other traditional scheduling strategies are carried out.The result shows the validity and reasonability of the multi-stage strategy.

Heuristics in Language Comprehension

We used a sentence-picture matching task to demonstrate that heuristics can influence language comprehension. Interpretation of quantifier scope ambiguous sentences such as Every kid climbed?a tree was investigated. Such sentences are ambiguous with respect to the number of trees inferred;either several trees were climbed or just one. The availability of the NOUN VERB NOUN (N-V-N) heuristic, e.g., KID CLIMB TREE, should contribute to the interpretation of how many trees were climbed. Specifically, we hypothesized that number choices for these stimuli would be predicted by choices previously made to corresponding (full) sentences. 45 participants were instructed to treat N-V-N triplets such as KID CLIMB TREE as telegrams and select a picture, regarding the quantity (“several” vs. “one”) associated with tree. Results confirmed that plural responses to quantifier scope ambiguous sentences significantly predict increased plural judgments in the picture-matching task. This result provides empirical evidence that the N-V-N heuristic, via conceptual event knowledge, can influence sentence interpretation. Furthermore, event knowledge must include the quantity of participants in the event (especially in terms of “several” vs. “one”). These findings are consistent with our model of language comprehension functioning as “Heuristic first, algorithmic second.” Furthermore, results are consistent with judgment and decision making in other cognitive domains.

Local Search Heuristics for NFA State Minimization Problem

In the present paper we introduce new heuristic methods for the state minimization of nondeterministic finite automata. These methods are based on the classical Kameda-Weiner algorithm joined with local search heuristics, such as stochastic hill climbing and simulated annealing. The description of the proposed methods is given and the results of the numerical experiments are provided.

Research on the Generation Path of Heuristic Teaching Language Based on Positive Psychology Theory

As the essence of traditional Chinese educational thought,heuristic teaching has gradually enriched and devel-oped its ideas through the continuous efforts of educational researchers of the past dynasties.In front-line teach-ing,heuristic teaching,as a teaching principle that can help teachers and students to interact and learn,undoubtedly plays an important role in students’acquisition of knowledge and scientific thinking activities.But teachers’understanding of heuristic teaching is not the same.In actual teaching,there are obvious gaps in language ability among different teachers.This research aims to enrich the heuristic teaching theory based on the perspective of psychology,and provide a certain theoretical basis for teacher training.Through the research and analysis of the generation path of the heuristic teaching language,this paper helps more teachers realize the advantages of the heuristic teaching language,improve their own heuristic teaching language level,and provide reference for the scientific teaching evaluation standard.We apply the heuristic instruction language approach proposed in this study and apply it to real classrooms.In the application,it can be clearly found that the students’thinking and participation have been strengthened,and the teaching effect has been significantly improved com-pared with the past,which indicates that the heuristic teaching language generation path in this study has certain practical application value.

Using Heuristics to the Controller Placement Problem in Software-Defined Multihop Wireless Networking

Solving the controller placement problem (CPP) in an SDN architecture with multiple controllers has a significant impact on control overhead in the network, especially in multihop wireless networks (MWNs). The generated control overhead consists of controller-device and inter-controller communications to discover the network topology, exchange configurations, and set up and modify flow tables in the control plane. However, due to the high complexity of the proposed optimization model to the CPP, heuristic algorithms have been reported to find near-optimal solutions faster for large-scale wired networks. In this paper, the objective is to extend those existing heuristic algorithms to solve a proposed optimization model to the CPP in software-defined multihop wireless networking (SDMWN).Our results demonstrate that using ranking degrees assigned to the possible controller placements, including the average distance to other devices as a degree or the connectivity degree of each placement, the extended heuristic algorithms are able to achieve the optimal solution in small-scale networks in terms of the generated control overhead and the number of controllers selected in the network. As a result, using extended heuristic algorithms, the average number of hops among devices and their assigned controllers as well as among controllers will be reduced. Moreover, these algorithms are able tolower the control overhead in large-scale networks and select fewer controllers compared to an extended algorithm that solves the CPP in SDMWN based on a randomly selected controller placement approach.

Guest Editorial:Advances in Bio-inspired Heuristics for Computing

Bio-inspired computing (BIC), short for biologically inspired computing, is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence. The field of bio-inspired computing brings together researchers from many disciplines, including biology, computer science, mathematics, physics and genetics.

Heuristics for Improving Operational Performance of Permutation Circulati on-type Vehicle Routing System

This paper discusses an optimization of operating a p ermutation circulation-type vehicle routing system (PCVRS, for short), in w hich several stages are located along by a single loop, and a fleet of vehicles travels on the loop unidirectionally and repeatedly. Traveling on the loop, each vehicle receives an object from the loading stage and then carries it to a cert ain processing stage, or receives an object from a certain processing stage and then carries it to the unloading stage per a turnaround. No passing is allowed f or the vehicles on the loop (from which the system is called permutation, and th is restriction may cause interferences between vehicles). Material handling systems such as PCVRS are actually encountered in flexible man ufacturing systems and in automated storage/retrieval systems. In this paper, we propose a heuristic algorithm for operating the PCVRS, which i ncorporates a new scheduling method for the vehicles with the SPT (shortest proc essing time) numbering of jobs and a round-robin manner of allocating jobs to t he stages, aiming to reduce interferences between the vehicles. We also give num erical results with respect to system performances attained by the heuristic. Description of the system The PCVRS consists of a set of n v vehicles V={V 1,V 2,...,V n v}, a set of n s, processing stages S p={S 1,S 2,...,S n s}, a loading stage S 0 and an unloading stage S n s +1. We denote by S=S p∪{S 0,S n s+l} the set of all the stages. The vehicles travel on a single loop unidirectionany and repeated ly. The system layout is depicted in Fig.1. There is a set of n jobs J={J 1,J 2,...,J n} to be processed b y the vehicles. Each job consists of two tasks: That is, each vehicle receives a n object from S 0 and then carries it to S l with a certain l∈{1,2, ...,n s} (a throw-in job), or receives an object from S l with a certain l∈{1,2,...,n s} and then carries it to S n s+1 (a throw-out job ) per a turnaround. The loop consists of buffer zones BZ(l) and travel zones TZ(l) (see Fig. 1). Each buffer zone BZ(l) is placed in front of stage S l, l=0,1,..., n s, n s+1, in order to avoid a collision between vehicles (i.e., the syste m adopts the so-called zone control strategy). A heuristic algorithm We develop a heuristic algorithm to obtain a good performance for the PCVRS. An operation π={A/B/C} for the PCVRS consists of three decision factors: (A) Numbering jobs Jobs are loaded into S 0 according to an assending order of job numbers. In this paper, we use the following rules to number jobs: SPT: Order jobs in the shortest processing time rule, i.e., P 1≤P 2≤...≤P n for the set of jobs J={J 1,J 2,...,J n}, rather than the FCFS numbering (i.e., number jobs in first-come-first-served order). The SPT rule intends to reduce interferences between two adjacent vehicles at stages. (B) Allocating jobs to stages For the purpose of balancing loads of processing stages, we adopt the following to allocate jobs to the stages: ORDER: Allocate n jobs to n s, processing stages by an in-order manner , i.e., let l(i) be the index of processing stage allocated job J i by ORDER, it holds that l(i)=n s+1-(i-[(i-1)/n s]n s).(1) The ORDER rule intends to process jobs parallel at stages as many as possible. (C) Scheduling vehicles The following method for scheduling vehicles under ORDER rule is already known: Fig.1 The vehicle ro uting system, PCVRS Fig.2 Mean turnaroun d times by heuristics Unchange: Assign n jobs to n v vehicles such that let k(i) be the i ndex of vehicle processing job J i, then k(i)= i-[(i-1)/n v]n v.(2) In csse of n v≥n s, mod (n v,n s)=0 or n v<n s, mod (n s,n v)=0 (mod(x,y) is the remainder of x/y), the number of interferences between vehicles is minimized at stage S 1 under Unchange sche dules, while in the other cases it is not [Lu et al. (2001a)]. Therefore, in t his paper, we develop a new scheduling method of the vehicles, denoted by Ex change, to modify Unchange schedules. Note