An asymptotically optimal public parking lot location algorithm based on intuitive reasoning

In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand,this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem.Guided by the idea of intuitive reasoning,we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions.First,Selective Attention Mechanism(SAM)is introduced to reduce the search space by adaptively focusing on the important information in the features.Then,Quantum Annealing(QA)algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution.Experiments on the parking lot location dataset in Luohu District,Shenzhen,show that the proposed method has improved the accuracy and running speed of the solution,and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.

A reposition algorithm for e-hailing based on quantum annealing and intuitive reasoning

Currently,the challenge lies in the traditional intelligent algorithm’s ability to effectively address the e-hailing repositioning issue.Accurately identifying the underlying characteristics in extensive traffic data within a limited timeframe is difficult,ultimately preventing the achievement of the most optimal solution.This paper suggests a hybrid computing architecture involving reinforcement learning and quantum annealing based on intuitive reasoning.Intuitive reasoning aims to enhance performance in scenarios with poor system robustness,complex tasks,and diverse goals.A deep learning model is constructed,trained to extract scene features,and combined with expert knowledge,then transformed into a quantum annealable form.The final strategy is obtained using a D-wave quantum computer with quantum tunneling effect,which helps in finding optimal solutions by jumping out of local suboptimal solutions.Based on 400000 real data,four algorithms are compared:minimum-cost flow,sequential markov decision process,hot-dot strategy,and driver-prefer strategy.The average total revenue increases by about 10%and vehicle utilization by about 15%in various scenarios.In summary,the proposed architecture effectively solves the e-hailing reposition problem,offering new directions for robust artificial intelligence in big data decision problems.

Dempster-Shafer Evidence Theory and Study of Some Key Problems

As one of the most important mathematical methods, the Dempster-Shafer（D-S）evidence theory has been widely used in date fusion, risk assessment, target identification, knowledge reasoning,and other fields. This paper summarized the development and recent studies of the explanations of D-S model, evidence combination algorithms, and the improvement of the conflict during evidence combination, and also compared all explanation models,algorithms, improvements, and their applicable conditions. We are trying to provide a reference for future research and applications through this summarization.