scholarly journals Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding

2019 ◽  
Author(s):  
Keisuke Okumura ◽  
Manao Machida ◽  
Xavier Défago ◽  
Yasumasa Tamura
Keyword(s):  
Finite Time ◽  
Time Window ◽  
Small Time ◽  
Experimental Results ◽  
Global Communication ◽  
Path Finding ◽  
Priority Inversion ◽  
Practical Applications ◽  
Novel Approach ◽  
Multi Agent

The Multi-agent Path Finding (MAPF) problem consists in all agents having to move to their own destinations while avoiding collisions. In practical applications to the problem, such as for navigation in an automated warehouse, MAPF must be solved iteratively. We present here a novel approach to iterative MAPF, that we call Priority Inheritance with Backtracking (PIBT). PIBT gives a unique priority to each agent every timestep, so that all movements are prioritized. Priority inheritance, which aims at dealing effectively with priority inversion in path adjustment within a small time window, can be applied iteratively and a backtracking protocol prevents agents from being stuck. We prove that, regardless of their number, all agents are guaranteed to reach their destination within finite time, when the environment is a graph such that all pairs of adjacent nodes belong to a simple cycle of length 3 or more (e.g., biconnected). Our implementation of PIBT can be fully decentralized without global communication. Experimental results over various scenarios confirm that PIBT is adequate both for finding paths in large environments with many agents, as well as for conveying packages in an automated warehouse.

scholarly journals Counterplanning using Goal Recognition and Landmarks

2018 ◽  
Author(s):  
Alberto Pozanco ◽  
Yolanda E-Martín ◽  
Susana Fernández ◽  
Daniel Borrajo
Keyword(s):  
Automated Planning ◽  
Experimental Results ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Novel Approach ◽  
Multi Agent ◽  
Goal Recognition ◽  
Domain Independent

In non-cooperative multi-agent systems, agents might want to prevent the opponents from achieving their goals. One alternative to solve this task would be using counterplanning to generate a plan that allows an agent to block other's to reach their goals. In this paper, we introduce a fully automated domain-independent approach for counterplanning. It combines; goal recognition to infer an opponent's goal; landmarks' computation to identify subgoals that can be used to block opponents' goals achievement; and classical automated planning to generate plans that prevent the opponent's goals achievement. Experimental results in several domains show the benefits of our novel approach. 

scholarly journals Finite-Time Consensus of Nonlinear Multi-Agent Systems via Impulsive Time Window Theory: A Two-Stage Control Strategy

2021 ◽  
Author(s):  
Teng Long ◽  
SS Yang ◽  
Qianzhu Wang ◽  
Lianghao Ji ◽  
Xiaofeng Liao
Keyword(s):  
Finite Time ◽  
Impulsive Control ◽  
Time Window ◽  
Control Period ◽  
Switching Topology ◽  
Multi Agent Systems ◽  
Two Stage ◽  
Agent Systems ◽  
Control Stage ◽  
Multi Agent

Abstract This paper concentrates on the problem of finite-time consensus of nonlinear multi-agent systems (MASs) via impulsive time window theory: a two-stage control (TSC) strategy. The TSC divide the whole control period into two parts, separately variable impulsive control stage and finite-time consensus control stage. Different from the general single-stage control, TSC can adjust the time period of impulsive control and finite-time control dynamically according to the practical application requirements. The variable impulsive control is also discussed in this paper. Comparing with the traditional fixed impulsive theory, the impulsive sampling time occurs randomly within the impulsive time window, providing much more flexibility to the system. In addition, the switching topology scheme is introduced in this paper to strengthen the stability of the MASs. Finally, two numerical simulation examples on the leaderless case and leader-following case are given to demonstrate the theoretical analysis.

scholarly journals Finite-time consensus of nonlinear multi-agent systems via impulsive time window theory: a two-stage control strategy

2021 ◽  
Vol 105 (4) ◽  
pp. 3285-3297
Author(s):  
Teng Long ◽  
Shasha Yang ◽  
Qianzhu Wang ◽  
Lianghao Ji ◽  
Xiaofeng Liao
Keyword(s):  
Control Strategy ◽  
Finite Time ◽  
Time Window ◽  
Multi Agent Systems ◽  
Two Stage ◽  
Agent Systems ◽  
Multi Agent

scholarly journals Generalized Target Assignment and Path Finding Using Answer Set Programming

2017 ◽  
Author(s):  
Van Nguyen ◽  
Philipp Obermeier ◽  
Tran Cao Son ◽  
Torsten Schaub ◽  
William Yeoh
Keyword(s):  
Scale Up ◽  
Answer Set Programming ◽  
Specific Information ◽  
Path Finding ◽  
Practical Applications ◽  
Target Assignment ◽  
Specific Order ◽  
Multi Agent ◽  
Unequal Number ◽  
Answer Set

In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications such as autonomous warehouse systems. We address this limitation by generalizing TAPF to allow for (1)~unequal number of agents and tasks; (2)~tasks to have deadlines by which they must be completed; (3)~ordering of groups of tasks to be completed; and (4)~tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based method can be efficient and can scale up to solve practical applications.

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

Mining discriminative patches for script identification in natural scene images

2021 ◽  
Vol 40 (1) ◽  
pp. 551-563
Author(s):  
Liqiong Lu ◽  
Dong Wu ◽  
Ziwei Tang ◽  
Yaohua Yi ◽  
Faliang Huang
Keyword(s):  
Neural Networks ◽  
Experimental Results ◽  
The Other ◽  
Natural Scene ◽  
Fixed Size ◽  
Script Identification ◽  
Aspect Ratios ◽  
Novel Approach ◽  
Public Datasets ◽  
Natural Scene Images

This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.

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